IEEE Radar Conference 2021 Radar on the Move

May 8-14, 2021

Document Date: Friday 7th May, 2021 12:32 Contents at a Glance

1 Welcome from the Chairs 6

2 Welcome from the Technical Program Co-Chairs 7

3 Organizing Committee 9

4 Technical Review Committee 12

5 Technical Program 17

6 Plenary Speakers 36

7 Special Events 43

8 IEEE Awards 52

9 Student Paper Finalists 62

10 Radar Systems Panel Members 63

11 Corporate Patrons 64

12 Exhibitors 65

13 Tutorials 66

14 Radar Summer School 97

15 Abstracts 104

Author Index 173

Keyword Index 180

1 Contents In Detail

1 Welcome from the Chairs 6

2 Welcome from the Technical Program Co-Chairs 7

3 Organizing Committee 9

4 Technical Review Committee 12 4.1 Track Chairs ...... 12 4.2 Reviewers ...... 14

5 Technical Program 17 5.1 Daily Schedule ...... 17 5.2 Program Summary ...... 20

6 Plenary Speakers 36 6.1 Fred Moorefield: Radar in an Era of Diminishing Spectrum ...... 37 6.2 Maria Sabrina Greco: Cognitive Radars - The Present and the Envisioned Future . . 38 6.3 Dan Hicok: The FAA’s Evolution of Surveillance Technology ...... 40 6.4 Julie Jackson and Shannon Blunt: Moving Radar Education Forward ...... 41

7 Special Events 43 7.1 Women In Engineering Event ...... 43 7.2 Six Keys to Success ...... 43 7.3 Wicks Celebration of Life ...... 44 7.4 Young Professionals Panel Session: A Spectrum of Careers in Radar ...... 46 7.5 Keynote Speaker and Awards Ceremony ...... 47 7.6 Exhibitor Bingo Awards ...... 48 7.7 Government/Industry Panel ...... 48

8 IEEE Awards 52 8.1 Robert T. Hill Best Dissertation Award ...... 52 8.2 Fred Nathanson Memorial Radar Award ...... 53 8.3 AES Society Pioneer Award ...... 54 8.4 AESS Industrial Innovation Award ...... 55 8.5 Warren White Award for Excellence in Radar Engineering ...... 56 8.6 M. Barry Carlton Award ...... 57 8.7 IEEE Dennis J. Picard Medal for Radar Technologies and Applications ...... 58 8.8 AESS Engineering Scholarships ...... 59 8.9 AESS Fellows ...... 59 8.10 Harry Mimno Best Paper Award ...... 60 8.11 Radar Challenge ...... 61

9 Student Paper Finalists 62

10 Radar Systems Panel Members 63

11 Corporate Patrons 64

2 IEEE RADARCONF 2021 CONTENTS IN DETAIL

12 Exhibitors 65

13 Tutorials 66 13.1 Overview ...... 66 13.2 Recent Developments in Maritime Radar Detection [MA-1] ...... 67 13.3 Introduction to Airborne Ground-Moving Target Indicator Radar [MA-2] ...... 68 13.4 Introduction to Automotive Radars [MA-3] ...... 69 13.5 Convex Optimization for Adaptive Radar [MA-4] ...... 70 13.6 Radar Tracking: A Long-Standing Cooperation Between Industry and Academia [MA-5] ...... 72 13.7 Analytic Combinatorics for Multi-Object Tracking [MA-6] ...... 74 13.8 Passive Radar – From Target Detection to Imaging [MP-1] ...... 76 13.9 Advanced Inverse Synthetic Aperture Radar Imaging [MP-2] ...... 77 13.10 Digital Array Radar [MP-3] ...... 78 13.11 Deep Learning Applications for Radar Systems with MATLAB [MP-4] ...... 81 13.12 Efficient Spectral Access for Radar and Communications [MP-5] . . . . . 82 13.13 Virtual RF Environments to Support Advanced Radar Mode Development [FA-1] . . 84 13.14 Deep Learning for Radio Frequency Automatic Target Recognition (ATR) [FA-2] . . 87 13.15 Multi-function Radar Resources Management [FA-3] ...... 89 13.16 Micro-Doppler Signatures: Principles, Analysis and Applications [FA-4] ...... 90 13.17 Terahertz and Sub-Terahertz Automotive Radar: Emerging Technologies and Chal- lenges [FA-5] ...... 91 13.18 Bistatic and Multistatic Radar Imaging [FA-6] ...... 94

14 Radar Summer School 97 14.1 Daily Schedule ...... 97 14.2 Instructors ...... 98

15 Abstracts 104 Estimation and Detection in Challenging Target Scenarios ...... 105 Special Session: 20 Years of Waveform Diversity: Progress Toward Realizable Systems . 106 Special Session: Next-Gen Automotive Radars : Opportunities and Challenges ...... 108 Special Session: Quantum Radar Theory and Practice (Part I) ...... 110 Estimation and Detection in Clutter and Interference ...... 111 Waveforms & Waveform Diversity ...... 113 Automotive Applications of Radar ...... 115 Special Session: Quantum Radar Theory and Practice (Part II) ...... 117 Adaptive Signal Processing ...... 118 Special Session: Deep Learning and AI for Radar (Part I) ...... 120 UAV Detection and Classification ...... 121 Terahertz & mmWave Radar ...... 123 ECCM & Interference Cancellation ...... 125 Special Session: Deep Learning and AI for Radar (Part II) ...... 126 Special Session: Short-Range Radar Applications to Security ...... 128 Medical and Biological Applications ...... 130 MIMO Radar Techniques ...... 131 Special Session: Machine Learning for Future Radar Technology ...... 133 Radar Recognition Techniques and Applications ...... 135

3 IEEE RADARCONF 2021 CONTENTS IN DETAIL

Special Session: Biomedical and e-Healthcare Applications of Radar ...... 136 Array Processing ...... 138 Cognitive Radar & Machine Learning ...... 140 Target Localization and Classification at short ranges ...... 142 Software Defined Radar & Low-cost radar ...... 144 Special Session: Spaceborne SAR Missions: State of the Art and Future Developments . . 146 Multichannel and Multistatic Passive Radar ...... 148 Spectrum Sharing ...... 149 Special Session: Synergistic Radar Signal Processing and Tracking ...... 151 Information Extraction from SAR images ...... 153 Passive Radar Applications ...... 155 Resource Management ...... 156 Tracking and Fusion ...... 158 Radar Imaging ...... 159 Special Session: Multistatic and Networked Radar - a Tribute to Viktor Chernyak . . . . . 161 Special Session: Digital Array Radar ...... 163 Clutter and Target Signatures ...... 164 Synthetic Aperture Radar Imaging ...... 166 Multistatic and Distributed MIMO systems ...... 167 Antennas and Components ...... 169 Ground-Penetrating and Sounding ...... 171

Author Index 173

Keyword Index 180

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1 Welcome from the Chairs

Welcome to the virtual 2021 IEEE Radar Conference hosted by the IEEE Atlanta Section and the IEEE Aerospace and Electronic Systems Society (AESS). This conference promises an engaging program through lectures chosen from hundreds of submissions, plenary talks, the radar summer school, a diverse set of tutorials, an exciting radar challenge, a Women in Engineering (WiE) discussion on “Being the First”, and a new government and industry panel session! In addition, there will be opportunities to learn more about the IEEE Young Professionals, schedule appointments with exhibitors, and network in this virtual environment. The rich technical program, themed under Radar on the Move, includes topics in radar techniques, multisensor exploitation, applications and phenomenology, systems and subsystems, as well as a series of special sessions.

Enjoy your time at this virtual conference, and we will do our best to make it enjoyable and techni- cally rewarding.

Conference Chair Conference Vice Chair Kristin Bing Theresa Brunasso Georgia Tech Research Institute D&S Microwave

6 IEEE RADARCONF 2021 2 WELCOME FROM THE TECHNICAL PROGRAM CO-CHAIRS

2 Welcome from the Technical Program Co-Chairs

Dear Attendees,

It is our great pleasure to welcome you to the 2021 IEEE Radar Conference. The conference was originally proposed to be held in person in Atlanta, Georgia, USA. However, the world in which we live has changed significantly since the original proposal and the conference is being held virtually. The organizing committee has strived to structure the virtual conference so that attendees have an experience similar to that of an in person conference.

Building a technical program of this year’s virtual conference that closely represents that of an in person conference has been a challenge. The technical program committee was constructed of subject matter experts (SMEs) in the various aspects of radar systems from throughout the world. The technical areas of interest in radar were organized into 23 tracks and two or three SMEs were assigned to manage the review of papers in their track. An average of 4.3 reviews were obtained for each paper. The conference received 269 submissions and 203 papers were accepted for publication for an acceptance rate of 75%. The distinction between poster and regular papers was dropped due to the virtual format and all accepted papers are considered regular papers. Those papers were organized into 40 sessions in four presentation tracks.

In order to improve the overall quality of papers in the conference and ensure that papers in areas considered critical by the technical committee were part of the program, Laura Anitori (TNO), Luke Rosenberg (Defense Science & Technology Group, Australia), and Graeme Smith (JHU Ap- plied Physics Laboratory) were appointed as Special Session Chairs to identify the critical areas and invited organizers for special session in those areas. A total of 12 specials sessions were organized and are part of the technical program. Those special sessions cover both consolidated and emerging areas and were built to offer quite diverse points of view from industrial, research, and even historical aspects. We believe that this approach to special sessions has made a much better conference. The technical program is the result of many hours of hard work by the Technical Program Committee, and we can proudly state that the impressive technical program accounts for key issues of radar theory/practice and embraces many of the challenging aspects of radar systems for civilian, defense, and homeland security applications.

The contributors to the 2021 IEEE Radar Conference come from academia, government, industry, and research facilities, 29 countries, and 5 major regions. Track chairs, peer reviewers, and session chairs were selected from throughout the world. Thus, 2021 IEEE Radar Conference is truly an international event.

The conference program also incorporates a two-day Radar Summer School covering diverse and fundamental topics central to the field of Radar and providing a good warm-up to the more advanced Radar Tutorials being held during the 2021 IEEE Radar Conference. The Tutorial program includes 17 talks by recognized radar experts and provides attendees with the opportunity to take a deeper dive into topics most interesting to our community. These virtual classes connect attendees and instructors for in-depth learning, with demonstrations and/or hands-on materials. Last but not least, four plenary talks by well-known and distinguished speakers are planned to kick-off the technical program.

As in the past years, the 2021 IEEE Radar Conference hosts a student paper competition with

7 IEEE RADARCONF 2021 2 WELCOME FROM THE TECHNICAL PROGRAM CO-CHAIRS awards to recognize the best student papers. In addition, the conference features the organization of the Radar Challenge, a series of events that enables participants to experience the magic of radar in a personal, tangible and experiential way. In keeping the theme of recognition of younger radar engineers, a Celebration of Life Tribute to Mike Wicks will be part of the conference. Mike has been a mentor and benefactor to numerous members of the radar community. Mike died in December 2020 and one of his final acts as a benefactor to the radar community was agrantfor students for travel to all future radar conferences.

The organizers feel deeply indebted to all the authors, presenters, reviewers, invited speakers, exhibitors, and sponsors without whom this event could not have been possible. We are delighted to wish all of you a very fruitful virtual conference and look forward to stimulating productive interactions amongst the speakers, exhibitors, colleagues, and the broader virtual audience. After all, radar is on the move and we cannot stop it!!!!

With our warmest regards,

W. Dale Blair and Fabiola Colone

8 IEEE RADARCONF 2021 3 ORGANIZING COMMITTEE

3 Organizing Committee

Chair Vice Chair

Kristin Bing Theresa Brunasso Georgia Tech D&S Microwave Research Institute Technical Program Chairs Tutorials Chairs

W. Dale Blair Fabiola Colone Chris Barnes Braham Himed Georgia Tech Sapienza University of Georgia Institute Air Force Research Research Institute Rome of Technology Laboratory Special Sessions Chairs

Laura Anitori Luke Rosenberg Graeme Smith TNO DS&T Group, Australia The Ohio State University Plenary Chairs

Bill Melvin Jacqueline Fairley Georgia Tech Georgia Tech Research Institute Research Institute

9 IEEE RADARCONF 2021 3 ORGANIZING COMMITTEE

Student Program Chairs

Jim Skala Kellie McConnell Marshall Hopkins Georgia Tech Georgia Tech Georgia Tech Research Institute Research Institute Research Institute Patron Chairs Exhibits Chairs

Andy Register Joe Guerci Audrey Paulus Rikai Huang Georgia Tech Information Systems Georgia Tech Georgia Tech Research Institute Laboratories Research Institute Research Institute Publications Chairs Finance Chairs

Stephen Conover Sevgi Gurbuz Jennifer Palmer Jenny Reed Argos Intelligence University Alabama Georgia Tech Georgia Tech Tuscaloosa Research Institute Research Institute Virtual Arrangements Gov/Industry Publicity Chairs Chair Panel Chair

Brian Conant Joshua Kovitz Clayton Kerce Mike Noble Georgia Tech Georgia Tech Georgia Tech L3 Harris Research Institute Research Institute Research Institute

10 IEEE RADARCONF 2021 3 ORGANIZING COMMITTEE

WiE Luncheon Chairs

Dana Fitzgerald Anne Costolanski Julie Jackson Georgia Tech Georgia Tech Air Force Institute of Research Institute Research Institute Technology European Liaisons Asia Pacific Liaison

Maria S. Greco Hugh Griffiths Joe Fabrizio University of Pisa University College DS&T Group, Australia London AESS/GRSS Atlanta Chapter Volunteers

Ryan Bales Marsal Bruna Austin Foote Georgia Tech Georgia Tech Georgia Tech Research Institute Research Institute Research Institute

Brian Mulvaney Craig Schlottmann Georgia Tech Georgia Tech Research Institute Research Institute

11 IEEE RADARCONF 2021 4 TECHNICAL REVIEW COMMITTEE

4 Technical Review Committee

4.1 Track Chairs

The conference thanks the following distinguished experts for their invaluable help with the review process within specific technical areas.

Techniques

Adaptive Signal Processing Prof. Antonio De Maio Prof. Fulvio Gini Dr. Micheal Picciolo Array Processing Prof. Elias Aboutanios Prof. Braham Himed Classification & Identification Dr. Matthew Ritchie Dr. Francesco Fioranelli Cognitive Radar & Machine Learning Dr. Kristine Bell Prof. Nathan Goodman Prof. Birsen Yazici Detection & Estimation Prof. Hongbin Li Prof. Marco Lops Prof. Stéphanie Bidon ECCM & Interference Cancellation Dr. Ram Raghavan Prof. Danilo Orlando Imaging Prof. Marco Martorella Dr. Brian Rigling Resource Management Dr. Alexander Charlish Prof. Raviraj Adve Multisensor Exploitation

Multistatic, Networked, & Distributed Prof. Daniel O’Hagan Prof. Debora Pastina Dr Mohamed Jahangir Passive Radar Prof. Pierfrancesco Lombardo Dr. Diego Cristallini Spectrum Sharing Prof. Daniel Bliss Dr. Aboulnasr Hassanien Prof. Shannon Blunt Tracking & Fusion Dr. Peter Willett Dr. Paolo Braca Dr. Stefano Coraluppi

12 IEEE RADARCONF 2021 4 TECHNICAL REVIEW COMMITTEE

Waveforms & Waveform Diversity Prof. Augusto Aubry Dr. Frank Robey Dr. Laurent Savy

Applications & Phenomenology

Automotive & Transportation Prof. Christian Waldschmidt Dr. Kumar V. Mishra Medical & Biological Dr. Fauzia Ahmad Prof. Kevin Chetty Over the Horizon Radar Dr. Geoffrey San-Antonio Weather & Environmental Sensing Dr. Mark Yeary Dr. Luca Baldini Signatures, Clutter & Interference Prof. Simon Watts Characterization and Modeling Prof. Sabrina Greco Dr. James Sangston Space, Air, Sea & Ground-Based Prof. Hugh Griffiths Dr. Stefan V. Baumgartner Systems & Subsystems

Antennas Dr. Alan Fenn Dr. Daniel Rabideau Components: Transmitters, Receivers, and Processors Dr. Lorenzo Lo Monte Dr. Carmine Clemente mm-wave & THz Dr. Lam Nguyen Prof. Marina Gashinova Software Defined Radar & Low-cost radar Prof. Piotr Samczynski Dr. Anthony Martone Special Sessions

20 Years of Waveform Diversity: Prof. Shannon Blunt Progress Toward Realizable Systems Dr. Eric Mokole Biomedical and e-Healthcare applications of Radar Prof. Kevin Chetty Prof. Rob Piechocki Deep Learning & AI for Radar Dr. Sevgi Zubeyde Gurbuz Dr. Graeme Smith Digital Array Radar Dr. Salvador H. Talisa Dr. Kenneth W. O’Haver Machine Learning for Future Radar Technology Dr. Anthony Martone Prof. Justin Metcalf

13 IEEE RADARCONF 2021 4 TECHNICAL REVIEW COMMITTEE

Multistatic and Networked Radar: Prof. Hugh Griffiths A Tribute to Viktor Chernyak Dr. Alfonso Farina Next-Gen Automotive Radars: Dr. Igal Bilik Opportunities and Challenges Dr. Sandeep Rao Quantum radar theory and practice Dr. Fred Daum Dr. Bhashyam Balaji Short-Range Radar Applications to Security Prof. Alessio Balleri Dr. Victor Chen Spaceborne SAR Missions: Dr. Alberto Moreira State of the Art and Future Developments Dr. Dirk Geudtner Synergistic Radar Signal Processing and Tracking Prof. Florian Meyer Dr. Erik Leitinger

4.2 Reviewers

The conference thanks the following individuals for participating in the technical review process.

Kamaruddin Abdul Ghani Stefan Baumgartner Gang Chen Pia Addabbo Jonathan Bechter Kevin Chetty Raviraj Adve Jonathan Becker Domenico Ciuonzo Fauzia Ahmad Kristine Bell Carmine Clemente Thomas L. Ainsworth Adel Belouchrani Stefano Coraluppi Jabran Akhtar Gerald Benitz Brian Cordill Mohammad Al-Khaldi Stéphanie Bidon Bill Correll Jr Hazza Alharbi Igal Bilik Pepijn Cox Murtaza Ali Filippo Biondi Diego Cristallini Chris Allen Jonathan Blakely Guolong Cui Muhannad Almutiry Giovanni Paolo Blasone Fengzhou Dai Moeness Amin Shannon Blunt Thomas Dallmann Daniel Andre Carlo Bongioanni Anthony Damini Sebastien Angelliaume Jerome Bourassa Fred Daum Laura Anitori Andre Bourdoux Mark E Davis Martin Ankel Fabrice Boust Michael Davis Mike Antoniou Paolo Braca Jim Day Riccardo Ardoino Mattia Brambilla Andrea De Martino Gregory Arlow Paul Brennan Jacco de Wit Augusto Aubry Michael Callahan Dilan Dhulashia Canan Aydogdu Siyang Cao Roberto Di Candia Chris Baker Tan Cao Emre Ertin John Ball Amerigo Capria Aaron Evers Alessio Balleri Vincenzo Carotenuto Joe Fabrizio Yaakov Bar-Shalom Batu Chalise Alfonso Farina John Barrett Alexander Charlish Charles Farthing

14 IEEE RADARCONF 2021 4 TECHNICAL REVIEW COMMITTEE

Giancarmine Fasano Colin Horne Simon Maskell Alessio Fascista Nicholas Host Eric Mason Xiang Feng Yulin Huang Davide Massaro Thomas Feuillen Albert Huizing Dietmar Matthes Francesca Filippini Christos Ilioudis Gregory Mazzaro Francesco Fioranelli Michael Inggs Michael McBeth Georg Fischer Julie Jackson Reid McCargar Mark Fitzpatrick Mohammed Jahangir Mark McClure Goffredo Foglia Maria-Pilar Jarabo-Amores Patrick McCormick Terry Foreman S. Hamed Javadi Jay McDaniel Marco Frasca Yuan Jiang William Melvin Gordon Frazer Robert Jonsson Justin Metcalf Domenico Gaglione Ravi Kadlimatti Florian Meyer Yongchan Gao Joshua Kantor William Miceli Dmitriy Garmatyuk Seyed Mohammad Karbasi Leonardo Maria Millefiori David Garren Stephane Kemkemian Kumar Vijay Mishra Joseph Garry Nicolai Kern Eric Mokole Samuele Gelli Ali Khenchaf David Money Zhe Geng Peter Knott Stefania Monni Dirk Geudtner Dusan Kocur Alberto Moreira Selenia Ghio Alexander Koelpin John Mower Christoph Gierull Oleg Krasnov Ram Mohan Narayanan Fulvio Gini Jeffrey Krolik Willie Nel Elisa Giusti Krzysztof Kulpa Michael Newey Nathan Goodman Mario La Manna Xavier Neyt Felix Govaers Lan Lan Brian Ng Timo Grebner Toufik Laroussi Ruben Nocelo Lopez Maria Greco Julien Le Kernec Anders Erik Nordsjo Hugh Griffiths Mark Leifer Idar Norheim-Nass Ronny Guendel Erik Leitinger Benjamin Nuss Ali Gurbuz Benjamin Lewis Nicholas O’Donoughue Sevgi Gurbuz Guchong Li Kenneth O’Haver Daniel Gusland Haobo Li Lisa Osadciw Jeffrey Hall Hongbin Li Luca Pallotta Chengpeng Hao Jun Liu Debora Partina Matthew Harger Weijian Liu Nial Peters Stephen Harman Marco Lops Nikita Petrov Aboulnasr Hassanien Konstantin Lukin Nazzareno Pierdicca Stephen Hayward Tom Lukowski Jean E. Piou Yuan He David Luong Stefano Pirandola Daniel Hebert Junhu Ma Iole Pisciottano Ronald Helmick Marco Maffei Dinesh Rajan Brendan Hennessy Mateusz Malanowski Sreeraman Rajan Andrew Herschfelt Linlin Mao Shobha Ram Braham Himed Salvatore Maresca Karthik Ramasubramanian Franz Hlawatsch Evgeny Markin Sandeep Rao Folker Hoffmann Anthony Martone Brandon Ravenscroft Rudolf Hoffmann Marco Martorella Jeremy Reed

15 IEEE RADARCONF 2021 4 TECHNICAL REVIEW COMMITTEE

Jiaying Ren Charles Smith Simon Wagner Christ Richmond Graeme Smith Dan Wang Brian Rigling Giovanni Soldi Fangzhou Wang Matthew Ritchie Gasti Solodky Guohua Wang Artit Rittiplang Giacomo Sorelli Jianping Wang Frank Robey Patipathi Srihari Junling Wang Ric Romero Krzysztof Stasiak Xiangrong Wang Yu Rong Christian Steffes Simon Watts Fabian Roos Daniel Stevens David Wikner Massimo Rosamilia Jim Stiles Peter Willett Paul Rosen Pietro Stinco John Williams Luke Rosenberg John Stralka Chane Williamson Mayazzurra Ruggiano Pasquale Striano Jack Winters Cenk Sahin Guohao Sun Philipp Wojaczek Takuya Sakamoto Hongbo Sun Michael Woollard Piotr Samczynski Lennart Svensson Linjie Yan Jim Sangston Ryuhei Takahashi Jing Yang Jim Sangston Salvador Talisa Mark Yeary Fabrizio Santi Paul Techau Jianxin Yi Agnès Santori Chris Teixeira Wei Yi Augustin Saucan Richard Tillman Robert Young Theresa Scarnati Luca Timmoneri Xianxiang Yu Steffen Schieler Sonia Tomei Michael Zatman Johannes Schlichenmaier Faruk Uysal Evan Zaugg Robert Schmid Ferran Valdes Crespi Renyuan Zhang Armin Schneider Piet van Genderen Xudong Zhang Pascale Sevigny Krishna Venkataraman Yimin Zhang Jeffrey Shapiro Shahar Vileval Le Zheng Hasan Sharifi Shelly Vishwakarma Junwei Zhou Greg Showman David Vitali Zhenghan Zhu Mark Sletten Vadym Volkov

16 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

5 Technical Program

5.1 Daily Schedule

Tuesday, May 11

Time (UTC-4) Virtual Room A Virtual Room B Virtual Room C Virtual Room D

8:00-11:20 Plenary Speakers

11:20-11:40 Break Break Break Break

11:40-1:20 Estimation and Special Session: 20 Special Session: Special Session: Detection in Years of Waveform Next-Gen Quantum Radar Challenging Target Diversity: Progress Automotive Radars: Theory and Scenarios Toward Realizable Opportunities and Practice (Part I) Chairs: Francesca Systems Challenges Chairs: Bhashyam Filippini and Chairs: Shannon Chairs: Igal Bilik Balaji and Hongbin Li Blunt and Eric and Sandeep Rao Fred Daum Mokole

1:20-2:20 WIE Event Break Break Break

2:20-4:20 Estimation and Waveforms and Automotive Special Session: Detection in Clutter Waveform Diversity Applications of Quantum Radar and Interference Chairs: Frank Robey Radar Theory and Practice Chairs: Stephanie and Fabrizio Santi Chairs: Kumar Vijay (Part II) (includes Bidon and Batu Mishra and panel session) Krishna Chalise Christian Chairs: Bhashyam Waldschmidt Balaji and Fred Daum

4:20-5:20 Six Keys to Success

5:20 Wicks Celebration of Life

17 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Wednesday, May 12

Time (UTC-4) Virtual Room A Virtual Room B Virtual Room C Virtual Room D

8:00-9:40 Adaptive Signal Special Session: UAV Detection and Terahertz and Processing Deep Learning and Classification mmWave Radar Chairs: Micheal AI for Radar (Part I) Chairs: Carmine Chairs: Marina Picciolo and Luke Chairs: Sevgi Clemente and Mark Gashinova and Lam Rosenberg Zubeyde Gurbuz Govoni Nguyen and Graeme Smith

9:40-11:20 ECCM and Special Session: Special Session: Medical and Interference Deep Learning and Short-Range Radar Biological Cancellation AI for Radar (Part II) Applications to Applications Chairs: Danilo Chairs: Sevgi Security Chairs: Fauzia Orlando and Ram Zubeyde Gurbuz Chairs: Alessio Ahmad and Raghavan and Graeme Smith Balleri and Victor Moeness Amin Chen

11:20-11:40 Break Break Break Break

11:40-1:20 MIMO Radar Special Session: Radar Recognition Special Session: techniques Machine Learning Techniques and Biomedical and Chairs: Guolong Cui for Future Radar Applications e-Healthcare and Muralidhar Technology Chairs: Maria Applications of Rangaswamy Chairs: Anthony Sabrina Greco and Radar Martone and Justin Matthew Ritchie Chairs: Kevin Chetty Metcalf and Mohammud Bocus

1:20-2:20 Young Break Break Break Professionals Panel Session: A Spectrum of Careers in Radar

2:20-4:20 Array Processing Cognitive Radar Target Localization Software Defined Chairs: Elias and Machine and Classification Radar and Low-cost Aboutanios and Learning at Short Ranges radar Braham Himed Chairs: Kristine Bell Chairs: Kristine Bell Chairs: Piotr and Nathan and Nathan Samczynski and Goodman Goodman John Stralka

4:20 Keynote Speaker and Awards Ceremony

18 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Thursday, May 13

Time (UTC-4) Virtual Room A Virtual Room B Virtual Room C Virtual Room D

8:00-9:40 Special Session: Multichannel and Spectrum Sharing Synergistic Radar Spaceborne SAR Multistatic Passive Chairs: Daniel Bliss Signal Processing Missions: State of Radar and Aboulnasr Tracking the Art and Future Chairs: Hassanien Chairs: Erik Developments Pierfrancesco Leitinger and Chairs: Dirk Lombardo and Florian Meyer Geudtner and Daniel O’Hagan Alberto Moreira

9:40-11:20 Information Passive Radar Resource Tracking and Extraction from Chairs: Diego Management Fusion SAR Images Cristallini and Chairs: Raviraj Adve Chair: Dale Blair Chairs: Stefan V. Krzysztof Kulpa and Alexander Baumgartner and Charlish Birsen Yazici

11:20-11:40 Break Break Break Break

11:40-1:20 Radar Imaging Special Session: Special Session: Clutter and Target Chairs: Marco Multistatic and Digital Array Radar Signatures Martorella and Networked Radar - Chairs: Kenneth W. Chairs: Francesco Brian Rigling a Tribute to Viktor O’Haver and Fioranelli and Chernyak Salvador H. Talisa Simon Watts Chairs: Alfonso Farina and Hugh Griffiths

1:20-2:20 Exhibitor Bingo Break Break Break Awards

2:20-4:00 Synthetic Aperture Multistatic and Antennas and Ground-Penetrating Radar Imaging Distributed MIMO Components and Sounding Chairs: Julie systems Chairs: Alan Fenn Chairs: Lorenzo Lo Jackson and Chairs: Mohamed and Daniel Monte and Mark Laurent Savy Jahangir and Rabideau Yeary Debora Pastina

4:00-5:50 Government / Industry Panel

19 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

5.2 Program Summary

TUESDAY

Estimation and Detection in Challenging Target Scenarios Begins: 5/11/2021 11:40 Ends: 5/11/2021 13:20 Location: Virtual Room A Chaired by Francesca Filippini and Hongbin Li

1. Analyzing the Effective Coherent Integration Time for Space Surveillance Radar Processing by Rajat Awadhiya, Risto Vehmas 2. Entropy-Based Coherent Integration Method for Moving Target Detection Using Phased- MIMO Radar by Mingxing Wang, Xiaolong Li, Tao Fan, Zhi Sun, Chenyu Wang, Guolong Cui 3. Statistics of Vehicular Detectability for Cooperative Passive Coherent Location at Urban Crossroad by Saw James Myint, Steffen Schieler, Christian Schneider, Wim Kotterman, Gio- vanni Del Galdo, Reiner Thomä 4. A Novel Signal Power Based Multi-Targets Detection for FMCW Radar by Yuki Tachibana, Chenggao Han 5. Going Below and Beyond Off-the-Grid Velocity Estimation from 1-Bit Radar Measurements by Gilles Monnoyer de Galland, Thomas Feuillen, Luc Vandendorpe, Laurent Jacques

Special Session: 20 Years of Waveform Diversity: Progress Toward Realizable Systems Begins: 5/11/2021 11:40 Ends: 5/11/2021 13:20 Location: Virtual Room B Chaired by Shannon Blunt and Eric Mokole

1. Waveform Design for Sparse Signal Processing in Radar by Laura Anitori, Joachim Ender 2. Cognitive Radar for Waveform Diversity Utilization by Anthony Martone, Alexander Charlish 3. Practical Effects in Radar Transmitters and Their Effect on Spectrum by Hugh Griffiths 4. Practical Waveform Diversity Applications and Implementation Challenges by John Stralka, Daniel Thomas 5. Matched Correlation of Linear and Non-Linear Frequency-Modulated Waveforms for Far-Field TDOA-DoA in the Context of MFRFS by Josef Worms, Michael Kohler, Daniel O’Hagan

20 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Special Session: Next-Gen Automotive Radars : Opportunities and Challenges Begins: 5/11/2021 11:40 Ends: 5/11/2021 13:20 Location: Virtual Room C Chaired by Igal Bilik and Sandeep Rao

1. Multipath Ghost Targets Mitigation in Automotive Environments by Oren Longman, Shahar Villeval, Igal Bilik 2. Characterization of Some Interference Mitigation Schemes in FMCW Radar by Sandeep Rao, Anil Mani 3. Multiplexing of OFDM-Based Radar Networks by David Werbunat, Fabio Sgroi, Christina Knill, Benedikt Schweizer, Benedikt Meinecke, Rossen Michev, Jürgen Hasch, Christian Waldschmidt 4. Radar Interference Mitigation Through Active Coordination by Canan Aydogdu, Musa Furkan Keskin, Gisela K. Carvajal, Olof Eriksson, Hans Hellsten, Hans Herbertsson, Emil Nilsson, Mats Rydström, Karl Vanäs, Mustafa Mete 5. Sparse Step-Frequency MIMO Radar Design for Autonomous Driving by Shunqiao Sun, Lifan Xu, Nathan Jeong

Special Session: Quantum Radar Theory and Practice (Part I) Begins: 5/11/2021 11:40 Ends: 5/11/2021 13:20 Location: Virtual Room D Chaired by Bhashyam Balaji and Fred Daum

1. When Should We Use Likelihood Ratio Target Detection with QTMS Radar and Noise Radar? by David Luong, Bhashyam Balaji, Sreeraman Rajan 2. Simulating Quantum Radar with Brownian Processes by Marco Frasca, Alfonso Farina 3. Quantum Radar and Noise Radar Concepts by Konstantin Lukin 4. Energetic Considerations in Quantum Target Ranging by Athena Karsa, Stefano Pirandola 5. Optimal Quantum Radar vs. Optimal Classical Radar with Full Polarization Antennas by Fred Daum, Arjang Noushin, Jim Huang

Estimation and Detection in Clutter and Interference Begins: 5/11/2021 14:20 Ends: 5/11/2021 16:20 Location: Virtual Room A Chaired by Stephanie Bidon and Batu Krishna Chalise

1. Improved Target Detection in Spiky Sea Clutter Using Sparse Signal Separation by Malcolm Wong, Elias Aboutanios, Luke Rosenberg 2. Distributed GLRT-Based Detection of Target in SIRP Clutter and Noise by Batu Chalise, Kevin Wagner 3. Optimal Target Detection for Random Channel Matrix-Based Cognitive Radar/Sonar by Tou- seef Ali, Christ Richmond 4. Exploiting the Phase of a Bio-Inspired Receiver by Krasin Georgiev 5. Quantized Time Delay for Target Localization in Cloud MIMO Radar by Zhen Wang, Qian He, Rick S. Blum 6. MIMO Radar Moving Target Detection in Clutter Using Supervised Learning by Shabing Ye, Qian He, Xiaorui Wang

21 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Waveforms & Waveform Diversity Begins: 5/11/2021 14:20 Ends: 5/11/2021 16:20 Location: Virtual Room B Chaired by Frank Robey and Fabrizio Santi

1. A Waveform Independent Approach to Detecting Targets in Clutter with Coherent Nonidenti- cal Pulses by Byrant Moss, Terry Foreman 2. Waveform Selection for a Scanning Radar in the Maritime Environment by Azam Mehboob, Luke Rosenberg, Kutluyil Dogancay, Brian Ng, Mike Hartas 3. Computationally Efficient Joint-Domain Clutter Cancellation for Waveform-Agile Radar by Christian Jones, Brandon Ravenscroft, James Vogel, Suzanne Shontz, Thomas Higgins, Kevin Wagner, Shannon Blunt 4. Minimum PSL Discrete-Phase Waveform Design with Length-Change Mismatched Filter by Rujun Hu, Yi Bu, Xianxiang Yu, Guolong Cui, Zhengxin Yan 5. A Formal Study of the Doppler Tolerance of Costas and Sudoku Waveforms by Bill Correll Jr, Travis Bufler, Christopher N. Swanson, Ram Narayanan 6. Design of Constant Modulus Sequence Set with Good Doppler Tolerance via Minimizing WISL by Hui Qiu, Tao Fan, Yi Bu, Xianxiang Yu, Guolong Cui

Automotive Applications of Radar Begins: 5/11/2021 14:20 Ends: 5/11/2021 16:20 Location: Virtual Room C Chaired by Kumar Vijay Mishra and Christian Waldschmidt

1. Code Diversity for Range Sidelobe Attenuation in PMCW and OFDM Radars by Marc Bauduin, André Bourdoux 2. Automotive Synthetic Aperture Radar Imaging Using TDM-MIMO by Masoud Farhadi, Rein- hard Feger, Johannes Fink, Thomas Wagner, Andreas Stelzer 3. Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Test- ing of Autonomous Driving by Anthony Ngo, Max Paul Bauer, Michael Resch 4. Parallelized Instantaneous Velocity and Heading Estimation of Objects Using Single Imaging Radar by Nihal Singh, Dibakar Sil, Ankit Sharma 5. Adversarial Interference Mitigation for Automotive Radar by Chenming Jiang, Tianyi Chen, Bin Yang 6. Position and Velocity Fusion Using Multiple Monostatic Radar Sensors for Automotive Appli- cations by Christian Schüßler, Marcel Hoffmann, Randolf Ebelt, Ingo Weber, Martin Vossiek

Special Session: Quantum Radar Theory and Practice (Part II) Begins: 5/11/2021 14:20 Ends: 5/11/2021 16:20 Location: Virtual Room D Chaired by Bhashyam Balaji and Fred Daum

1. Quantum-Correlated Noise Radar with Phase-Sensitive Amplification by Jonathan Blakely 2. Microwave Quantum Radarś Alphabet Soup: QI, QI-MPA, QCN, QCN-CR by Jeffrey Shapiro 3. Quantum Radar - What Is It Good For? by Robert Jonsson, Martin Ankel 4. Panel Session

22 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

WEDNESDAY

Adaptive Signal Processing Begins: 5/12/2021 8:00 Ends: 5/12/2021 9:40 Location: Virtual Room A Chaired by Micheal Picciolo and Luke Rosenberg

1. Solution for Complex Amplitude in LCD Removal Algorithm by Hanna Gjermo Chomitz, James Lievsay, Julie Ann Jackson 2. Knowledge-Aided Data-Driven Radar Clutter Representation by Yi Feng, Chayut Wongkamthong, Mohammadreza Soltani, Yuting Ng, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Robert Calderbank, Muralidhar Rangaswamy, Vahid Tarokh 3. Robust Adaptive Beamforming Based on the Direct Biconvex Optimization Modeling by Xiny- ing Zou, Qiping Zhang, Weijian Zhang, Jinfeng Hu 4. Efficient Implementation of Iterative Adaptive Approach Based on GPU Framework forRadar Super-Resolution Imaging by Jie Li, Yongwei Zhang, Yongchao Zhang, Deqing Mao, Yulin Huang, Jianyu Yang 5. Enhancing Space-Time Adaptive Processing Through the Slepian Transform by Lisa Osadciw, Daniel Hebert

Special Session: Deep Learning and AI for Radar (Part I) Begins: 5/12/2021 8:00 Ends: 5/12/2021 9:40 Location: Virtual Room B Chaired by Sevgi Zubeyde Gurbuz and Graeme Smith

1. Quality of Service Based Radar Resource Management Using Deep Reinforcement Learning by Sebastian Durst, Stefan Brüggenwirth 2. Human Micro-Doppler Signature Classification in the Presence of a Selection of Jamming Signals by Dilan Dhulashia, Matthew Ritchie, Shelly Vishwakarma, Kevin Chetty 3. Complex-Valued Neural Networks for Synthetic Aperture Radar Image Classification by Theresa Scarnati, Benjamin Lewis 4. Fool the COOL - on the Robustness of Deep Learning SAR ATR Systems by Simon Wagner, Chandana Panati, Stefan Brüggenwirth 5. Deep Learning Based Phaseless SAR Without Born Approximation by Samia Kazemi, Birsen Yazici

23 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

UAV Detection and Classification Begins: 5/12/2021 8:00 Ends: 5/12/2021 9:40 Location: Virtual Room C Chaired by Carmine Clemente and Mark Govoni

1. A Comparison of Convolutional Neural Networks for Low SNR Radar Classification of Drones by Holly Dale, Christopher J. Baker, Michail Antoniou, Mohammed Jahangir, George Atkinson 2. Neural Network Based Drone Recognition Techniques with Non-Coherent S-Band Radar by Engin Kaya, Gulay Buyukaksoy Kaplan 3. Extraction and Analysis of Micro-Doppler Signature in FMCW Radar by Soorya Peter, Vinod Reddy 4. Small Drone Detection Using Airborne Weather Radar by William Blake, Isaiah Burger 5. UAV Micro-Doppler Signature Analysis Using FMCW Radar by Vinod Reddy, Soorya Peter

Terahertz & mmWave Radar Begins: 5/12/2021 8:00 Ends: 5/12/2021 9:40 Location: Virtual Room D Chaired by Marina Gashinova and Lam Nguyen

1. A Terahertz Radar Feature Set for Device-Free Gesture Recognition by Liying Wang, Zongyong Cui, Yiming Pi, Changjie Cao, Zongjie Cao 2. Teragogic : Open Source Platform for Low Cost Millimeter Wave Sensing and Terahertz Imag- ing by Adrien Chopard, Frédéric Fauquet, Jing Shun Goh, Mingming Pan, Anton Simonov, Olga Smolyanskaya, Patrick Mounaix, Jean-Paul Guillet 3. Open Radar Initiative: Large Scale Dataset for Benchmarking of Micro-Doppler Recognition Algorithms by Daniel Gusland, Jonas Myhre Christiansen, Børge Torvik, Francesco Fioranelli, Sevgi Zubeyde Gurbuz, Matthew Ritchie 4. Effects of Reference Frequency Harmonic Spurs in FMCW Radar Systems by Jingzhi Zhang, Sherif Ahmed, Amin Arbabian 5. High-Resolution Drone-Borne SAR Using Off-the-Shelf High-Frequency Radars by Ali Bekar, Michail Antoniou, Christopher J. Baker

ECCM & Interference Cancellation Begins: 5/12/2021 9:40 Ends: 5/12/2021 11:20 Location: Virtual Room A Chaired by Danilo Orlando and Ram Raghavan

1. Electronic Protection Mitigation Techniques Against Transmit Waveform Shaped Noise Jam- mers by Alex Feltes, Ric Romero 2. Target Signature Extraction Using Truncated Singular Value Decomposition for Electronic Protection by Heitor Albuquerque, Ric Romero 3. Detection and Mitigation of Mutual RFI in C-Band SAR : A Case Study of Chinese GaoFen-3 by Zongsen Lv, Ning Li, Zhengwei Guo, Jianhui Zhao 4. Adaptable RF/Analog Transmit Leakage Canceller for Simultaneous Transmit/Receive Appli- cations by Peter Stenger, Raymond Power 5. Doppler Filter Bank Design for Non-Uniform PRI Radar in Signal-Dependent Clutter by Tao Fan, Yukai Kong, Mingxing Wang, Xianxiang Yu, Guolong Cui, Liwei Zhang

24 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Special Session: Deep Learning and AI for Radar (Part II) Begins: 5/12/2021 9:40 Ends: 5/12/2021 11:20 Location: Virtual Room B Chaired by Sevgi Zubeyde Gurbuz and Graeme Smith

1. Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-Cloud Data by Peter Svenningsson, Francesco Fioranelli, Alexander Yarovoy 2. Reinforcement Learning for Waveform Design by Graeme Smith, Taylor Reininger 3. Through-Wall Human Activity Classification Using Complex-Valued Convolutional Neural Net- work by Xiang Wang, Pengyun Chen, Hangchen Xie, Guolong Cui 4. Sign Language Recognition Using Micro-Doppler and Explainable Deep Learning by James McCleary, Laura Parra García, Christos Ilioudis, Carmine Clemente 5. Complex-Valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation by Alexander Fuchs, Johanna Rock, Mate Toth, Paul Meissner, Franz Pernkopf

Special Session: Short-Range Radar Applications to Security Begins: 5/12/2021 9:40 Ends: 5/12/2021 11:20 Location: Virtual Room C Chaired by Alessio Balleri and Victor Chen

1. An LSTM Approach to Short-Range Personnel Recognition Using Radar Signals by Zhenghui Li, Julien Le Kernec, Francesco Fioranelli, Olivier Romain, Lei Zhang, Shufan Yang 2. Enhanced Micro-Doppler Feature Analysis for Drone Detection by Yimin Zhang, Xingyu Xiang, Yi Li, Genshe Chen 3. Harmonic Radar for Differentiating Between Friend and Foe by Tanisha Gosain, Shobha Ram 4. DVB-S Based Passive Radar for Short Range Security Application by Francesca Filippini, Oc- tavio Cabrera, Carlo Bongioanni, Fabiola Colone, Pierfrancesco Lombardo 5. Multi-Frequency RF Sensor Data Adaptation for Motion Recognition with Multi-Modal Deep Learning by Mohammad Mahbubur Rahman, Sevgi Zubeyde Gurbuz

Medical and Biological Applications Begins: 5/12/2021 9:40 Ends: 5/12/2021 11:20 Location: Virtual Room D Chaired by Fauzia Ahmad and Moeness Amin

1. Word-Level Sign Language Recognition Using Linguistic Adaptation of 77 GHz FMCW Radar Data by Mohammad Mahbubur Rahman, Robiulhossain Mdrafi, Ali Cafer Gurbuz, Evie Malaia, Chris Crawford, Darrin Griffin, Sevgi Zubeyde Gurbuz 2. Radar-Based Efficient Gait Classification Using Gaussian Prototypical Networks by Usman Niazi, Souvik Hazra, Avik Santra, Robert Weigel 3. Multiple Moving Targets Heartbeat Estimation and Recovery Using Multi-Frequency Radars by Yu Rong, Kumar Vijay Mishra, Daniel W. Bliss 4. Sequential Classification of ASL Signs in the Context of Daily Living Using RF Sensingby Emre Kurtoglu, Ali Cafer Gurbuz, Evie Malaia, Darrin Griffin, Chris Crawford, Sevgi Zubeyde Gurbuz 5. Heartbeat Measurement with Millimeter Wave Radar in the Driving Environment by Chris Schwarz, Hunza Zainab, Soura Dasgupta, Justin Kahl

25 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

MIMO Radar Techniques Begins: 5/12/2021 11:40 Ends: 5/12/2021 13:20 Location: Virtual Room A Chaired by Guolong Cui and Muralidhar Rangaswamy

1. Cognitive-Driven Optimization of Sparse Array Transceiver for MIMO Radar Beamforming by Weitong Zhai, Xiangrong Wang, Syed A. Hamza, Moeness G. Amin 2. MIMO Radar Waveform Design via Deep Learning by Kai Zhong, Weijian Zhang, Qiping Zhang, Jinfeng Hu, Pengfei Wang, Xianxiang Yu, Qiyu Zhou 3. A Novel Towed Jamming Suppression with FDA-MIMO Radar by Siqi Li, Zhulin Zong, Yun Feng 4. New Coherent and Hybrid Detectors for Distributed MIMO Radar with Synchronization Errors by Cengcang Zeng, Fangzhou Wang, Hongbin Li, Mark Govoni 5. MIMO Radar Beampattern Formation with Spectral Coexistence via Sequential Convex Ap- proximation by Xianxiang Yu, Hui Qiu, Tao Fan, Yi Bu, Guolong Cui

Special Session: Machine Learning for Future Radar Technology Begins: 5/12/2021 11:40 Ends: 5/12/2021 13:20 Location: Virtual Room B Chaired by Anthony Martone and Justin Metcalf

1. Artificially Intelligent Power Amplifier Array (AIPAA): A New Paradigm in Reconfigurable Radar Transmission by Charles Baylis, Robert J Marks II, Austin Egbert, Casey Latham 2. Multi-Player Bandits for Distributed Cognitive Radar by William Howard, Charles Thornton, Anthony Martone, Michael Buehrer 3. RADGAN: Applying Adversarial Machine Learning to Track-Before-Detect Radar by Caleb Carr, Bibi Dang, Justin Metcalf 4. LTE Interference Effects on Radar Performance by Jordan Devault, Jacob Kovarskiy, Benjamin Kirk, Anthony Martone, Ram Narayanan, Kelly Sherbondy 5. Target Detection and Interference Mitigation in Future AI-Based Radar Systems by Hai Deng, Braham Himed

Radar Recognition Techniques and Applications Begins: 5/12/2021 11:40 Ends: 5/12/2021 13:20 Location: Virtual Room C Chaired by Maria Sabrina Greco and Matthew Ritchie

1. A Multi-Radar Architecture for Human Activity Recognition in Indoor Kitchen Environments by Ali Gorji, Thomas Gielen, Marc Bauduin, Hichem Sahli, André Bourdoux 2. A Robust Real-Time Human Activity Recognition Method Based on Attention-Augmented GRU by Qiang Jian, Shisheng Guo, Pengyun Chen, Peilun Wu, Guolong Cui 3. Micro-Doppler Signal Decomposition Using Second-Order Vertical Synchrosqueezing by Karol Abratkiewicz, Piotr Samczyński, Krzysztof Kulpa 4. Graph and Projection Pursuits Approach for Time Frequency Analysis by Bingcheng Li 5. Automatic Modulation Recognition for Overlapping Radar Signals Based on Multi-Domain SE-ResNeXt by Yehan Ren, Weibo Huo, Jifang Pei, Yulin Huang, Jianyu Yang

26 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Special Session: Biomedical and e-Healthcare Applications of Radar Begins: 5/12/2021 11:40 Ends: 5/12/2021 13:20 Location: Virtual Room D Chaired by Kevin Chetty and Mohammud Bocus

1. Continuous Human Activity Recognition for Arbitrary Directions with Distributed Radars by Ronny Gerhard Guendel, Matteo Unterhorst, Ennio Gambi, Francesco Fioranelli, Alexander Yarovoy 2. Augmenting Experimental Data with Simulations to Improve Activity Classification in Health- care Monitoring by Chong Tang, Shelly Vishwakarma, Wenda Li, Raviraj Adve, Simon Julier, Kevin Chetty 3. Interference Motion Removal for Doppler Radar Vital Sign Detection Using Variational Encoder-Decoder Neural Network by Mikolaj Czerkawski, Christos Ilioudis, Carmine Clemente, Craig Michie, Ivan Andonovic, Christos Tachtatzis 4. Physics-Aware Design of Multi-Branch GAN for Human RF Micro-Doppler Signature Synthesis by Mohammad Mahbubur Rahman, Sevgi Zubeyde Gurbuz, Moeness G. Amin 5. Application of MM-Wave Radar and Machine Learning for Post-Stroke Upper Extremity Motor Assessment by Edward Benavidez, Guy DeMartinis, Yining Wu, Andrew Gatesman

Array Processing Begins: 5/12/2021 14:20 Ends: 5/12/2021 16:20 Location: Virtual Room A Chaired by Elias Aboutanios and Braham Himed

1. DOA Estimation with Subarrays via Blind Source Separation Algorithm by Zhengxin Yan, Meng- meng Ge, Guolong Cui, Xianxiang Yu, Rujun Hu 2. Doubly-Toeplitz-Based Interpolation for Joint DoA-Range Estimation Using Coprime FDA by Ruisong Cao, Shengheng Liu, Zihuan Mao, Yongming Huang 3. Range-Dependent Beamforming Using Space-Frequency Virtual Difference Coarray by Tian- heng Ni, Shengheng Liu, Zihuan Mao, Yongming Huang 4. Radar Antenna Selection for Direction-of-Arrival Estimations by Arda Atalik, Mustafa Yilmaz, Orhan Arikan 5. Constant Beamwidth Receiving Beamforming Based on Template Matching by Ruitao Liu, Guolong Cui, Qinghui Lu, Xianxiang Yu, Lifang Feng, Jinghui Zhu 6. Virtual Array-Based Super-Resolution for Mechanical Scanning Radar by Linfeng Qiu, Yongchao Zhang, Yin Zhang, Yulin Huang, Jianyu Yang

27 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Cognitive Radar & Machine Learning Begins: 5/12/2021 14:20 Ends: 5/12/2021 16:20 Location: Virtual Room B Chaired by Kristine Bell and Nathan Goodman

1. Detecting Potential Performance Improvements in Cognitive Radar Systems by Austin Egbert, Adam Goad, Charles Baylis, Robert J Marks II, Anthony Martone 2. Constrained Online Learning to Mitigate Distortion Effects in Pulse-Agile Cognitive Radar by Charles Thornton, Michael Buehrer, Anthony Martone 3. The Value of Memory: Markov Chain Versus Long Short-Term Memory for Electronic Intelli- gence by Sabine Apfeld, Alexander Charlish, Gerd Ascheid 4. Spectral Gap Extrapolation and Radio Frequency Interference Suppression Using 1D UNets by Arun Nair, Akshay Rangamani, Lam Nguyen, Muyinatu Bell, Trac Tran 5. Quick Black Box Variational Inference Using Gaussian Cubature Integration Rules by Michał Meller 6. Error Correction Output Code-Based Radar Platform Motion Type Classification by Emirhan Ozmen, Fuat Cogun, Yasar Kemal Alp, Fatih Altiparmak

Target Localization and Classification at short ranges Begins: 5/12/2021 14:20 Ends: 5/12/2021 16:20 Location: Virtual Room C Chaired by Laura Anitori and Willie Nel

1. Investigation of Uncertainty of Deep Learning-Based Object Classification on Radar Spectra by Kanil Patel, William Beluch, Kilian Rambach, Adriana-Eliza Cozma, Michael Pfeiffer, Bin Yang 2. DEEPREFLECS: Deep Learning for Automotive Object Classification with Radar Reflections by Michael Ulrich, Claudius Gläser, Fabian Timm 3. Comparison of Different Approaches for Identification of Radar Ghost Detections in Auto- motive Scenarios by Yi Jin, Robert Prophet, Anastasios Deligiannis, Ingo Weber, Juan-Carlos Fuentes-Michel, Martin Vossiek 4. Moving Target Classification Based on Micro-Doppler Signatures via Deep Learning by Yonatan David Dadon, Shahaf Yamin, Stefan Feintuch, Haim Henry Permuter, Igal Bilik, Joseph Taberkian 5. WALDO Finds You Using Machine Learning: Wireless Adaptive Location and Detection of Objects by Aditya Singh, Pratyush Kumar, Vedansh Priyadarshi, Yash More, Aishwarya Das, Debayan Gupta 6. Deep Transfer Learning for WiFi Localization by Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham

28 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Software Defined Radar & Low-cost radar Begins: 5/12/2021 14:20 Ends: 5/12/2021 16:20 Location: Virtual Room D Chaired by Piotr Samczynski and John Stralka

1. Reverse Engineering the Soli Radar API for Military Applications by Khaled Basrawi, Richard Dill 2. An FPGA Based 24 GHz Radar Testbed for Physical-Layer Cyberattack Research by Onur Toker 3. Compressive Sensing Based Software Defined GPR for Subsurface Imaging by Yan Zhang, Daniel Orfeo, Dryver Huston, Tian Xia 4. Retrodirective Cross-Eye Jammer Implementation Using Software-Defined Radio (SDR) by Frans-Paul Pieterse, Warren du Plessis 5. Design of a New Low-Cost Miniaturized 5.8GHz Microwave Motion Sensor by Long Jin, Rui Cao, Dongsheng Li, Dandan Wang 6. Architecture Study for a Bare-Metal Direct Conversion Radar FPGA Testbench by Randall Summers, Mark Yeary, Hjalti Sigmarsson, Rafael Rincon

THURSDAY

Special Session: Spaceborne SAR Missions: State of the Art and Future Developments Begins: 5/13/2021 8:00 Ends: 5/13/2021 9:40 Location: Virtual Room A Chaired by Dirk Geudtner and Alberto Moreira

1. Copernicus and ESA SAR Missions by Dirk Geudtner, Nico Gebert, Michel Tossaint, Malcolm Davidson, Florence Heliere, Ignacio Navas Traver, Robert Furnell, Ramon Torres 2. NASA-ISRO SAR (NISAR) Mission Status by Paul Rosen, Raj Kumar 3. German Spaceborne SAR Missions by Alberto Moreira, Manfred Zink, Michael Bartusch, Eliz- abeth Nuncio Quiroz, Samuel Stettner 4. Overview of ALOS-2 and ALOS-4 L-Band SAR by Takeshi Motohka, Yukihiro Kankaku, Satoko Miura, Shinichi Suzuki 5. RADARSAT Constellation Mission Overview and Status by Guennadi Kroupnik, Daniel De Lisle, Stephane Côté, Mélanie Lapointe, Catherine Casgrain, Réjean Fortier

29 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Multichannel and Multistatic Passive Radar Begins: 5/13/2021 8:00 Ends: 5/13/2021 9:40 Location: Virtual Room B Chaired by Pierfrancesco Lombardo and Daniel O’Hagan

1. Complementary Direct Data Domain STAP for Multichannel Airborne Passive Radar by Diego Cristallini, Luke Rosenberg, Philipp Wojaczek 2. Performance Analysis of LTE Signals in RD-STAP Applications by Sureshan Suntharalingam, James Lievsay 3. An Adaptive Fusion Algorithm for Multistatic and Multichannel Passive Radar Detection by Junkang Wei, Junjie Li, Chunyi Song, Zhiwei Xu, Kai Ding 4. First Experimental Results on Multi-Angle DVB-S Based Passive ISAR Exploiting Multipolar Data by Fabrizio Santi, Iole Pisciottano, Debora Pastina, Diego Cristallini 5. Passive Multistatic Radar Imaging with Prior Information by Airas Akhtar, Bariscan Yonel, Birsen Yazici

Spectrum Sharing Begins: 5/13/2021 8:00 Ends: 5/13/2021 9:40 Location: Virtual Room C Chaired by Daniel Bliss and Aboulnasr Hassanien

1. Harmonic Mean SINR Maximization in a Cognitive Radar with Communication Spectrum Sharing by Junhui Qian, Luca Venturino, Marco Lops, Xiaodong Wang 2. Memory NLEQ Techniques to Mitigate Cross-Modulation Effects in Radar by Euan Ward, Bernard Mulgrew 3. Detection Performance of Embedded QPSK Onto LFM Waveform Guard Bands for RF Con- vergence by Jann Rohde, Ric Romero 4. Study of OAM for Communication and Radar by Daniel Orfeo, Dryver Huston, Tian Xia 5. Mutual Interference Alignment for Joint Phased Array Radar and Communication Systems by Bingqing Hong, Wenqin Wang, Hu Li

Special Session: Synergistic Radar Signal Processing and Tracking Begins: 5/13/2021 8:00 Ends: 5/13/2021 9:40 Location: Virtual Room D Chaired by Erik Leitinger and Florian Meyer

1. A Message Passing Based Adaptive PDA Algorithm for Robust Radio-Based Localization and Tracking by Alexander Venus, Erik Leitinger, Stefan Tertinek, Klaus Witrisal 2. Graph-Based Multiobject Tracking with Embedded Particle Flow by Wenyu Zhang, Florian Meyer 3. Joint Waveform and Guidance Control Optimization by Statistical Linearisation for Target Rendezvous by Alessio Benavoli, Alessio Balleri, Alfonso Farina 4. EM-Based Radar Signal Processing and Tracking by Alan Nussbaum, Byron Keel, William Dale Blair, Umakishore Ramachandran 5. Simultaneous Localization of a Receiver and Mapping of Multipath Generating Geometry in Indoor Environments by Christian Gentner, Markus Ulmschneider, Rostislav Karásek, Armin Dammann

30 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Information Extraction from SAR images Begins: 5/13/2021 9:40 Ends: 5/13/2021 11:20 Location: Virtual Room A Chaired by Stefan V. Baumgartner and Birsen Yazici

1. CNN for Radial Velocity and Range Components Estimation of Ground Moving Targets in SAR by Amir Hosein Oveis, Elisa Giusti, Selenia Ghio, Marco Martorella 2. Ship Classification Based on Sidelobe Elimination of SAR Images Supervised by Visual Model by Hongliang Zhu 3. Self-Supervised Speckle Reduction GAN for Synthetic Aperture Radar by Michael Newey, Pra- full Sharma 4. A Deep Deformable Residual Learning Network for SAR Image Segmentation by Chenwei Wang, Jifang Pei, Xiaoyu Liu, Yulin Huang, Jianyu Yang 5. Joint Image Formation and Target Classification of SAR Images by Charles Connors, Theresa Scarnati, Garrett Harris

Passive Radar Applications Begins: 5/13/2021 9:40 Ends: 5/13/2021 11:20 Location: Virtual Room B Chaired by Diego Cristallini and Krzysztof Kulpa

1. Passive Radar Based on LOFAR Radio Telescope for Air and Space Target Detection by Ma- teusz Malanowski, Konrad Jędrzejewski, Jacek Misiurewicz, Krzysztof Kulpa, Artur Gromek, Mariusz Pożoga, Julia Kłos, Aleksander Droszcz 2. UWB and WiFi Systems as Passive Opportunistic Activity Sensing Radars by Mohammud Bocus, Kevin Chetty, Robert Piechocki 3. Deinterleaving and Clustering Unknown Radar Pulses by Manon Mottier, Gilles Chardon, Frédéric Pascal 4. Passive Inverse Synthetic Aperture Radar Imaging from Non-Contiguous Frequency Bands by Aaron Brandewie, Robert Burkholder 5. Multi-Target Delay and Doppler Estimation in Bistatic Passive Radar Systems by Mohammed Rashid, Mort Naraghi-Pour

Resource Management Begins: 5/13/2021 9:40 Ends: 5/13/2021 11:20 Location: Virtual Room C Chaired by Raviraj Adve and Alexander Charlish

1. Joint Jamming Beam and Power Scheduling for Suppressing Netted Radar System by Dalin Zhang, Jun Sun, Wei Yi, Chengxin Yang, Yaqi Wei 2. Optimal Placement of Radars to Achieve Desired Spatially Nonuniform Probability of Detec- tion by Jase Furgerson, Dinesh Rajan 3. A Reconfigurable Resource Manager for Distributed Networked Radar by Reid McCargar, Graeme Smith 4. Time Budget Management in Multifunction Radars Using Reinforcement Learning by Petteri Pulkkinen, Tuomas Aittomäki, Anders Ström, Visa Koivunen 5. An Evaluation of Task and Information Driven Approaches for Radar Resource Allocation by Kristine Bell, Chris Kreucher, Muralidhar Rangaswamy

31 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Tracking and Fusion Begins: 5/13/2021 9:40 Ends: 5/13/2021 11:20 Location: Virtual Room D Chaired by Dale Blair

1. Exploiting Doppler in Bernoulli Track-Before-Detect by Du Yong Kim, Branko Ristic, Luke Rosen- berg, Robin Guan, Robin Evans 2. Online Multi-Target Tracking for Pedestrian by Fusion of Millimeter Wave Radar and Vision by Fucheng Cui, Yuying Song, Jingxuan Wu, Zhouzhen Xie, Chunyi Song, Zhiwei Xu, Kai Ding 3. Radar-Aided Navigation System for Small Drones in GPS-Denied Environments by Keith Klein, Faruk Uysal, Miguel Caro Cuenca, Matern Otten, Jacco de Wit 4. Distributed Registration and Multi-Target Tracking with Unknown Sensor Fields of View by Ziting Wang, Lei Chai, Wei Yi, Yongjian Liu 5. Message Passing Based Extended Objects Tracking with Measurement Rate and Extension Estimation by Yuansheng Li, Ping Wei, Yiqi Chen, Yifan Wei, Huaguo Zhang

Radar Imaging Begins: 5/13/2021 11:40 Ends: 5/13/2021 13:20 Location: Virtual Room A Chaired by Marco Martorella and Brian Rigling

1. 3D-ISAR Using a Single Along Track Baseline by Chow Yii Pui, Brian Ng, Luke Rosenberg, Tri- Tan Cao 2. Widely-Distributed Radar Imaging Based on Consensus ADMM by Ruizhi Hu, Bhavani Shankar Mysore Rama Rao, Ahmed Murtada, Mohammad Alaee-Kerahroodi, Björn Ottersten 3. A Hybrid Norm Regularization Approach for Radar Forward-Looking Angle Super-Resolution Imaging by Xingyu Tuo, Yin Zhang, Yulin Huang, Jianyu Yang 4. ISAR Translational Motion Compensation with Simultaneous Range Alignment and Phase Ad- justment in Low SNR Environments by Jixiang Fu, Mengdao Xing, Moeness G. Amin, Guangcai Sun 5. Efficient Radar Imaging Using Partially Synchronized Distributed Sensors by Ahmed Murtada, Ruizhi Hu, Mohammad Alaee-Kerahroodi, Udo Schroeder, Bhavani Shankar Mysore Rama Rao

Special Session: Multistatic and Networked Radar - a Tribute to Viktor Chernyak Begins: 5/13/2021 11:40 Ends: 5/13/2021 13:20 Location: Virtual Room B Chaired by Alfonso Farina and Hugh Griffiths

1. Multistatic and Networked Radar: Principles and Practice by Hugh Griffiths, Alfonso Farina 2. Application Experience on Radar Networking and Data Fusion Principles by Luca Tim- moneri, Alfonso Farina, Angela Incardona, Giovanni Golino, Antonio Graziano, Roberto Petrucci, Domenico Vigilante 3. Advanced Cognitive Networked Radar Surveillance by Mohammed Jahangir, Christopher J. Baker, Michail Antoniou, Benjamin Griffin, Alessio Balleri, David Money, Stephen Harman 4. Drone-Based 3D Interferometric ISAR Imaging by Elisa Giusti, Selenia Ghio, Marco Martorella 5. Fusion of Local Decisions Based on Rao Test in Resource-Constrained Sensor Networks by S. Hamed Javadi, Domenico Ciuonzo

32 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Special Session: Digital Array Radar Begins: 5/13/2021 11:40 Ends: 5/13/2021 13:20 Location: Virtual Room C Chaired by Kenneth W. O’Haver and Salvador H. Talisa

1. Investigation of Beam-Level Nonlinear Equalization in Digital Phased Arrays by Robert Schmid, Brian Gibbons, Kenneth O’Haver 2. Fully Digital Phased Array Development for Next Generation Weather Radar by Matthew Harger, M. David Conway, Henry Thomas, Mark Weber, Alex Morris, Ted Hoffmann, John Ben- dickson, Nathan Van Schaick 3. Update on an S-Band All-Digital Mobile Phased Array Radar by Mark Yeary, Robert Palmer, Caleb Fulton, Jorge Salazar, Hjalti Sigmarsson 4. Practical Demonstration of a Self-Calibration Technique Using an Element Level Digital Array by Cesar Lugo, Brian Kiedinger, Mitch Miller 5. Techniques for Digital Array Radar Planar Near-Field Calibration by Retrofit of an Analog Sys- tem by Thomas Williamson, Jason Whelan, Walter Disharoon, Paul Simmons, Jacob Houck, Brian Holman, Jacob Alward, Killian McDonald, Sean Kim, Dinal Andreasen

Clutter and Target Signatures Begins: 5/13/2021 11:40 Ends: 5/13/2021 13:20 Location: Virtual Room D Chaired by Francesco Fioranelli and Simon Watts

1. Measurements and Modeling of Heterogeneous Radar Clutter by Julie Jackson 2. Anisotropic Scatterer Models for Representing RCS of Complex Objects by Eric Huang, Cole- man Delude, Justin Romberg, Saibal Mukhopadhyay, Madhavan Swaminathan 3. Simulation of Ultra-Wideband Radar Returns from a Notional Sea Surface by Jimmy Alatishe 4. The Five-Domain-Six-Map Method for Signal Analysis in Over-the-Horizon Radar by Meihui Yan, Zhongtao Luo, Zishu He, Kun Lu

Synthetic Aperture Radar Imaging Begins: 5/13/2021 14:20 Ends: 5/13/2021 16:00 Location: Virtual Room A Chaired by Julie Jackson and Laurent Savy

1. AROMA SAR Refocus of Moving Targets Having Complicated Pitching Maneuvers by David Garren 2. Differentiable Synthetic Aperture Radar Image Formation and Generalized Minimum Entropy Autofocus by Joshua Kantor 3. Moving Target Imaging for Synthetic Aperture Radar via RPCA by Sean Thammakhoune, Bariscan Yonel, Eric Mason, Birsen Yazici, Yonina Eldar 4. Rotorcraft-Borne 3-D Forward-Looking MIMO SAR Imaging by Jiaying Ren, Jian Li, Lam Nguyen 5. SAR Fast Target Imaging in Sparse Field Based on AlexNet by Pan Zhang, Yinger Zhang, Yi Huang, Jiangtao Huangfu, Zhonghe Jin

33 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Multistatic and Distributed MIMO systems Begins: 5/13/2021 14:20 Ends: 5/13/2021 16:00 Location: Virtual Room B Chaired by Mohamed Jahangir and Debora Pastina

1. A Broadband Multistatic Radar for Trajectory Identification of Multiple Small Caliber Targets by Sean Lehman, Jae Jeon, Tammy Chang 2. A Fully Modular, Distributed FMCW MIMO Radar System with a Flexible Baseband Frequency by Adrian Figueroa, Niko Joram, Frank Ellinger 3. Information Diversity in Coherent MIMO Radars by Salvatore Maresca, Antonio Malacarne, Paolo Ghelfi, Antonella Bogoni 4. Antenna Placement for Distributed MIMO Radar with Different Missions in Different Subareas by Yao Wang, Wei Yi, Lingjiang Kong 5. Rx Beamforming for Long Baseline Multistatic Radar Networks by Rudolf Hoffmann, Nadav Neuberger, Risto Vehmas

Antennas and Components Begins: 5/13/2021 14:20 Ends: 5/13/2021 16:00 Location: Virtual Room C Chaired by Alan Fenn and Daniel Rabideau

1. Multi-Channel Feedarray Reflector Antenna Based Radar Concept for HRWS SAR Imaging by Javier del Castillo, Lara Orgaz, Quiterio Garcia, Nafsika Memeletzoglou, Carlos Biurrun-Quel, Carlos del-Río, Giovanni Toso, Ernesto Imbembo 2. The New Water-Cooled Cold Plate for Active Phased Array Antenna Using AM Technology by Toshihiro Kitazaki, Naoya Akaishi, Shigenao Tomiyasu, Genki Honma 3. A 2-Stage GaN IMFET Power Amplifier in an Embedded Heat Slug Laminate by Bo Zhao, Christopher Sanabria, Terry Hon, Alex Arayata 4. Design of a High-Order Dual-Wideband Superconducting Filter Using Stepped-Impedance Cross Structures by Xilong Lu, Weihua Wang, Yuhua Zhang, Delong Fu 5. Enhancing Frequency-Agile Radar Range Over a Broad Operating Bandwidth with Reconfig- urable Transmitter Amplifier Matching Networks by Justin Roessler, Adam Goad, Austin Eg- bert, Charles Baylis, Anthony Martone, Robert J Marks II, Benjamin Kirk

34 IEEE RADARCONF 2021 5 TECHNICAL PROGRAM

Ground-Penetrating and Sounding Begins: 5/13/2021 14:20 Ends: 5/13/2021 16:00 Location: Virtual Room D Chaired by Lorenzo Lo Monte and Mark Yeary

1. Development of a UAS-Based Ultra-Wideband Radar for Fine-Resolution Soil Moisture Mea- surements by Christopher Simpson, Shriniwas Kolpuke, Abhishek Awasthi, Tuan Luong, Sama Memari, Jie-Bang Stephen Yan, Ryan Taylor, Jordan Larson, Prabhakar Clement 2. Experimental Study on the Detection of Avalanche Victims Using an Airborne Ground Pene- trating Synthetic Aperture Radar by Alexander Grathwohl, Philipp Hinz, Ralf Burr, Maximilian Steiner, Christian Waldschmidt 3. An Improved Borehole Radar Fusion-Imaging Method for Heterogeneous Subsurface Sensing by Haining Yang, Shijia Yi, Na Li, Tingjun Li, Yujian Cheng, Qinghuo Liu 4. A Probe-Mounted Radar Downward-Looking Mapping Method for Mars Exploration by Xue Peng, Yongchao Zhang, Yin Zhang, Yulin Huang, Haiguang Yang, Jianyu Yang

35 IEEE RADARCONF 2021 6 PLENARY SPEAKERS

6 Plenary Speakers

Chaired by Jacqueline Fairley, Ph.D. and William L. Melvin, Ph.D.

The plenary session focuses on gaining the perspective of international authorities in areas where innovation and moving radar forward is of critical importance. The first presentation provides a forecast of the expected spectrum operating environment for radar given the insatiable need for bandwidth by commercial industry, and addresses key issues around technical innovation, policy, investment, and fielding of new technology. The second presentation discusses an important approach to enable enhanced radar operation in complex environments, especially those where spectrum is contested or in heavy demand, describing essential concepts and the state-of-the-art in cognitive radar. Developing the next generation of wide area surveillance radar is a challenging task confronting the FAA, and the corresponding forward-radar-thinking and search for technical innovation is the topic of the third talk in this session. Finally, it is the radar research community that moves radar forward, and radar education is essential in this regard, defining the topic for the fourth talk of this plenary session.

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36 IEEE RADARCONF 2021 6 PLENARY SPEAKERS

6.1 Fred Moorefield: Radar in an Era of Diminishing Spectrum

Radar has long had the luxury of operating in spectrum dedicated mostly to radar and operating in an environment that was predictable and stable. As the global demand for the wireless spectrum airwaves continues to grow and the world continues to wrestle to balance the many competing priorities for national security and economic prosperity (e.g., mobile broadband and air traffic control), the spectrum will continue to become more congested, contested and constrained. As the spectrum environment becomes more complex, and sharing scenarios implicate co-existence beyond compatibility between comms systems, future radar will have to be designed and developed to cope with this new environment. The plenary presentation will forecast the expected spectrum environment that current and future radar are expected to operate. The goal of the presentation is to inform radar technology innovation, investment, acquisition and policy.

Frederick D. Moorefield, Jr. is the Deputy Chief Information Officer for Command, Control, and Communications (C3), Office of the Secretary of Defense, Chief Information Officer. As DCIO, Mr. Moorefield provides technical expertise, oversight and broad guidance on policy, program- matic and technical issues relating to C3 to integrate and synchronize defense-wide communications programs. He also advises on efforts to achieve and maintain information dominance for the Department of Defense. He manages efforts defining DoD policies and strategies for design, architecture, interoperability standards, capability development and sustainment of critical command and control and communications for non-nuclear strategic strike, integrated missile defense, Defense and National Leadership Command Capabilities, and spectrum.

Mr. Moorefield joined Federal service in 1989 in the Air Force as a civil servant, where heserved for 19 years doing Research and Development and Acquisition at Wright Patterson Air Force Base Air Force Research Labs. He also served in the Defense Information Systems Agency at the Joint Spectrum Center for four years. He served as Technical Director and Director of Strategic Planning at the Air Force Spectrum Management Office and has been a member of the Senior Executive Corps since 2012.

His education includes a Bachelor degree in mathematics from Wilberforce University, located in Wilberforce Ohio and a Bachelor and Master of Electrical Engineering degree from the University of Dayton in Dayton Ohio.

37 IEEE RADARCONF 2021 6 PLENARY SPEAKERS

6.2 Maria Sabrina Greco: Cognitive Radars - The Present and the Envisioned Future

Over the past fifteen years, “cognition” has emerged as an enabling technology for incorporating learning and adaptivity on both transmit and receive to optimize or make more robust the radar performance in dynamic environments. The term ‘cognitive radar’ was introduced for the first time by Dr. Simon Haykin in 2006, but the foundations of the cognitive systems date back several decades to research on knowledge-aided signal processing, and adaptive radar design. The core of cognitive radar systems is the ‘perception-action cycle’, that is the feedback mechanism within the transceiver architecture that allows the radar system to learn information about a target and its environment and adapt its transmissions so as to optimize one or more missions, according to a desired goal. Such radar systems are sometimes called “fully-adaptive”, to highlight the main nov- elty of these new systems compared to the classical “adaptive” ones. The adaptivity is no longer only on receive but also on transmit. A truly cognitive radar should not be only able to adapt on the fly its transmission waveforms and parameters based on internal fixed rules and on what learned about the environment, but it should also be able to optimize these rules learning with time from its mistakes, as a biological system does (see for instance bath and dolphin sonar system). This is still a big challenge for radar experts.

This plenary speech will provide an overview of the main concept, and methods for modeling cognitive processes in a radar system. Some challenges to advancing the current state-of-the art will be discussed, and insights into future directions of research will be provided.

Maria Sabrina Greco graduated in Electronic Engineering in 1993 and received the Ph.D. degree in Telecommunication Engineering in 1998, from University of Pisa, Italy. From December 1997 to May 1998 she joined the Georgia Tech Research Institute, Atlanta, USA as a visiting research scholar where she carried on research activity in the field of radar detection in non-Gaussian background.

In 1993 she joined the Dept. of Information Engineering of the University of Pisa, where she is Full Professor since 2017. She’s IEEE fellow since Jan. 2011. She was co-recipient of the 2001 and 2012 IEEE Aerospace and Electronic Systems Society’s Barry Carlton Awards for Best Paper, co-recipient of 2019 EURASIP JASP Best Paper Award, and recipient of the 2008 Fred Nathanson Young Engineer of the Year award for contributions to signal processing, estimation, and detection theory and of IEEE AESS Board of Governors Exceptional Service Award for “Exemplary Service and Dedication and Professionalism, as EiC of the IEEE AES Magazine”. In May-June 2015 and in January-February 2018 she visited as invited Professor the Université Paris-Sud, CentraleSupélec, Paris, France.

38 IEEE RADARCONF 2021 6 PLENARY SPEAKERS

Processing on “Adaptive Waveform Design for Agile Sensing and Communication”, and lead guest editor of the special issue of International Journal of Navigation and Observation on” Modelling and Processing of Radar Signals for Earth Observation. She is Associate Editor of IET Proceed- ings – Sonar, Radar and Navigation, and IET-Signal Processing, and Editor in Chief of the Springer Journal of Advances in Signal Processing (JASP). She is member of the IEEE AESS Board of Gov- ernors and has been member of the IEEE SPS BoG (2015-17) and Chair of the IEEE AESS Radar Panel (2015-16). She has been as well SPS Distinguished Lecturer for the years 2014-2015, AESS Distinguished Lecturer for the years 2015-2020, and AESS VP Publications (2018-2020). She is now IEEE SPS Director-at-Large for Region 8.

Her general interests are in the areas of statistical signal processing, estimation and detection theory. In particular, her research interests include clutter models, coherent and incoherent detection in non-Gaussian clutter, CFAR techniques, radar waveform diversity and bistatic/mustistatic active and passive radars, cognitive radars. She co-authored many book chapters and about 200 journal and conference papers.

39 IEEE RADARCONF 2021 6 PLENARY SPEAKERS

6.3 Dan Hicok: The FAA’s Evolution of Surveillance Technology

Today and into the future, the Federal Aviation Administration (FAA) embraces innovation to deliver vital surveillance services for safe and efficient use in the National Airspace System (NAS). This talk explores the evolution of surveillance technology in the FAA and the future role of non- cooperative surveillance. With the deployment and integration of Automatic Dependent Surveil- lance Broadcast (ADS-B), the FAA has shifted its reliance on traditional sensors to a backup role. As this shift in surveillance usage continues to evolve, the FAA is investigating the use of non- cooperative radar for new roles. For example, “in-fill” radars could potentially provide coverage in wind farm areas and passive surveillance technology could be used in place of traditional radars.

Mr. Hicok currently serves as the Director of Surveillance Services in the FAA Program Management Organization. He is responsible for all surveillance acquisition programs including technical refresh programs for radar systems (ASR-8, ASR-9, ASR-11, BI-5, BI-6, Mode S, etc.), Surveillance and Broadcast Services (SBS) (Automatic Dependent Surveillance -Broadcast, Traffic Information Service – Broadcast, etc.), aircraft collision avoidance systems, and the FAA’s new Spectrum Effi- cient National Surveillance Radar program. Mr. Hicok was responsible for standing up this new Surveillance Services Directorate in 2019, an organization dedicated to the integration and acquisition of all surveillance related programs.

Formerly, Mr. Hicok served as the Chief Systems Engineer for the FAA’s Air Traffic Systems Directorate. In this role, he acted as an advisor to the Director and Integrator of new technologies envisioned by the NextGen organization as they transition into acquisition. Furthermore, he ensured strategic alignment with the FAA’s enterprise architecture roadmaps and programs in the directorate.

Mr. Hicok has spent the majority of his career as a systems engineering lead for key FAA acquisition programs including SBS Interval Management, Terminal Sequencing and Spacing, Airport Surveil- lance Detection Equipment Model-X Tech Refresh, Runway Status Lights, Airport Surveillance De- tection Equipment Model -3, Airport Movement Area Safety System, and Airport Surveillance Radar -model 11.

He has over 25 years of experience in industry and the Federal Government (FAA and DoD) in systems engineering, program management and technical leadership with air traffic control systems.

Mr. Hicok earned a Bachelor of Science in Aerospace Engineering and a Master of Science in Electrical Engineering from Virginia Tech.

Mr. Hicok is a member of the Air Traffic Control Association, the American Institute of Aeronau- tics and Astronautics, the International Council on Systems Engineering, and the FAA Managers Association.

40 IEEE RADARCONF 2021 6 PLENARY SPEAKERS

6.4 Julie Jackson and Shannon Blunt: Moving Radar Education Forward

Radars are complex systems that blend many traditional academic focus areas: electromagnetics, RF systems engineering and component technology, signal processing, controls, etc.. However, radar as an occupation is a smaller, more niche specialization of which too few students are made aware, especially compared to the large communications community and the current fever pitch of machine learning. This dichotomy of breadth and specialization raises the question of how could we better go about identifying and attracting talented students to the radar field. In this talk, we discuss the current state of radar education, industry needs as the “customer” in this context, and ideas for growing workforce-ready talent. With an emphasis on bringing industry and universities into closer alignment, join us and join the conversation so we can move radar education forward together.

Julie Ann Jackson is Professor of Electrical Engineering at the Air Force Institute of Technology (AFIT), where she joined the faculty in 2009. She earned the B.S.E.E. degree from Wright State University, Dayton, OH, in 2002 and the M.S. and Ph.D. degrees from The Ohio State University, Columbus, OH, in 2004 and 2009, respectively. Her graduate studies were funded with fellowships from the National Science Foundation, the Dayton Area Graduate Studies Institute, The Ohio State University, and the Ohio Board of Regents. Dr. Jackson has held internships at the Air Force Research Laboratory, Alphatech, Inc., Jacobs Sverdrup, and Bell Laboratories. She is a Senior Member of the IEEE, member of Eta Kappa Nu and Tau Beta Pi, and Chief Advisor for the Ohio Eta Chapter of Tau Beta Pi. Dr. Jackson serves on the IEEE AESS Radar Systems Panel as well as multiple technical program committees in the radar community. Dr. Jackson’s technical and educational contributions have been recognized with multiple awards including the 2019 IEEE Fred Nathanson Memorial Radar Award, 2019 AFIT Graduate School of Engineering and Manage- ment Research Award, 2019 Gage H. Crocker Outstanding Professor Award, Air University Civilian Quarterly Award in 2018, Southwestern Ohio Council for Higher Education (SOCHE) 2016 Faculty Excellence Award, and the 2012 Air Force Science, Technology, Engineering, and Mathematics Award for Outstanding Scientist/Engineer. Her research interests are in radar signal processing and imaging and exploitation of RF signals.

41 IEEE RADARCONF 2021 6 PLENARY SPEAKERS

Dr. Shannon D. Blunt is the Roy A. Roberts Distinguished Professor of Electrical Engineering & Computer Science (EECS) at the University of Kansas (KU), Director of the KU Radar Systems Lab (RSL), and Director of the Kansas Applied Research Lab (KARL). He received a PhD in electrical engineering from the University of Missouri in 2002, and from 2002 until he joined KU in 2005 he was with the Radar Division of the U.S. Naval Research Laboratory (NRL) in Washington, D.C. His research interests are in sensor signal processing and system design with a particular emphasis on waveform diversity and spectrum sharing techniques, having made a variety of contributions that have been deployed in operational radar and sonar systems.

Prof. Blunt received an AFOSR Young Investigator Award in 2008, the IEEE/AESS Nathanson Memorial Radar Award in 2012, was named a Fellow of the IEEE for “contributions to radar waveform diversity and design” in 2016, was appointed to the U.S. President’s Council of Advisors on Science & Technology (PCAST) in 2019, and received the 2020 IET Radar, Sonar & Navigation Premium Award. He has likewise received multiple teaching awards. He has over 180 refereed journal, conference, and book chapter publications, and 16 patents/patents- pending. He co-edited the books Principles of Waveform Diversity & Design (2010) and Radar & Communication Spectrum Sharing (2018).

He has served as a subject matter expert on topics related to radar spectrum management and sharing for DARPA, OUSD(R&E), the Air Force’s S&T 2030 Initiative, and the White House Office of Science & Technology Policy (OSTP), the latter as part of America’s Mid-Band Initiative Team (AMBIT) to enable nationwide 5G deployment. He recently served as Chair of the IEEE/AESS Radar Systems Panel and on the Board of Governors for the IEEE Aerospace & Electronic Systems Society (AESS). He is currently an Associate Editor for IEEE Transactions on Aerospace & Electronic Sys- tems and is on the Editorial Board for IET Radar, Sonar & Navigation. He was General Chair of the 2011 IEEE Radar Conference in Kansas City, Technical Chair of the 2018 IEEE Radar Conference in Oklahoma City, and will be a Technical Chair for the 2022 IEEE Radar Conference in New York City, along with being a member of the Program Committee for the MSS Tri-Service Radar Symposium series. He was Chair of the NATO SET-179 research task group on “Dynamic Waveform Diversity & Design” and a member of SET-182 on “Radar Spectrum Engineering & Management” and SET-227 on “Cognitive Radar”.

42 IEEE RADARCONF 2021 7 SPECIAL EVENTS

7 Special Events

7.1 Women In Engineering Event

When: Tuesday May 11, 1:20 - 2:20pm Where: Virtual Room A

Open to all conference attendees. Please join us for a presentation by the esteemed Dr. Serpil Ayasli, the first female recipient of the Warren D. White Award for Excellence in Radar Engineering. Dr. Ayasli will discuss the highlights and pitfalls related to “Being the First”. Many in the radar field have a claim to being a “First”: “First” to go to college; “First” to earn an engineering or science degree; “First” to balance a career and children. Dr. Ayasli will discuss her groundbreaking work that led to her receipt of the IEEE Warren D. White award, provide insight into those challenges that arise from being “First out of the Gate”, and provide advice for those pursuing their own “Firsts.”

Dr. Serpil Ayasli, former Associate Group Leader at MIT Lincoln Laboratory, received the 2008 Warren D. White Award for Excellence in Radar Engineering for her contributions to ultra-wideband radar for ground and foliage penetration. She was a corecipient of the IEEE/AESS 1996 M. Barry Carlton Award and was named an IEEE Fellow in 2002.

After her work at MIT as post-doctoral fellow in astrophysics from 1979 to 1982, Dr. Ayasli worked at MIT Lincoln Laboratory from 1982 until 2006. Her work at the Laboratory focused on air defense and surface surveillance radar research and development, including foliage penetration (FOPEN) and ground penetration (GPEN) radar. Dr. Ayasli and her team’s work at Lincoln Laboratory led to the first successful experimental proof of the feasibility of an advanced coherent FOPEN radar system that would enable detection and tracking of targets in foliage.

She holds a BS degree in electrical engineering and MS and PhD degrees in physics from the Middle East Technical University in Ankara, Turkey.

7.2 Six Keys to Success

When: Tuesday May 11, 4:20 - 5:20 Where: Virtual Room A

Whether you are just starting out in your career or deciding to chart a new career path, the Six Keys to Success can help you. After presenting a brief summary of her career, Mary Lynn Smith will dive into the COR GrUB as she discusses the Six Keys to Success. As she addresses each of the keys to success – Communication, Know Your Options, Manage Your Risks, Continue to Learn and Grow, Plan for the Unexpected, and Work/Life Balance, Mary Lynn will give you a path of what to focus on for each key, resources to help, and examples of what can happen can when you apply them. We hope to see you there.

43 IEEE RADARCONF 2021 7 SPECIAL EVENTS

Mary Lynn Smith is an Antenna & Microwave (A&M) Engineer/Project Engineer (PE)/A&M Engi- neering Manager for the Antenna Systems (AS) Division of Viasat, Inc. She is responsible for the electrical design of feeds, frequency selective surface (FSS) subreflectors, and antenna designs, managing cost, quality, performance, schedule, and technical aspects of projects, managing A&M Engineers, staffing, supporting proposals, maintaining tools, and identifying training.

7.3 Wicks Celebration of Life

When: Tuesday May 11, 5:20 Where: Virtual Room A

Join us for a celebration of the life of Michael C. “Mike” Wicks.

Michael Wicks (1959 – Dec. 9, 2020) was a gifted electronic engineer who had a profound impact on all who met and interacted with him. He spent the bulk of his career developing radar technology at the Air Force Research Laboratory (AFRL), Rome, NY, USA, rising to the level of a senior executive as the Senior Scientist for Sensor Signal Processing, Sensors Directorate. He retired from the Air Force in 2011, after 20 years of service, receiving a personal letter of commendation from President Barack Obama. Afterwards, he was then appointed to the Ohio Research Scholar Endowed Chair in Sensor Exploitation and Fusion, and Distinguished Re- search Engineer at the University of Dayton Research Institute (UDRI) in Dayton, OH, USA, where he taught and led research programs, supervising 35 doctoral students.

Mike was an incredible innovator and prolific source of ideas, many of which have now become mainstream, including knowledge-based signal processing, waveform diversity, sensors as robots, and frequency diverse arrays. Undoubtedly, his greatest legacy will be the countless young radar engineers worldwide who he has inspired and who are proud to have had him as a mentor. He was warm, energetic, selfless, always looking for ways to help those he encountered, even ifjust in a short conversation. His smile was contagious, and he had a mischievous sense of humor, but most of all he will be loved and remembered as our dearest friend and colleague.

To read more on his life and works, please see the Celebration of his Life memoriam published in the IEEE Aerospace and Electronic Systems Magazine, vol. 26, iss. 4, April 2021.

IEEE AESS Michael C. Wicks Radar Student Travel Grant Award

Mike Wicks knew he wanted to:

• Leave a legacy on his professional community • Nurture the next generation of radar engineers • Give Back to the IEEE Radar Community

The IEEE Foundation, IEEE’s philanthropic partner, in collaboration with IEEE AESS, had the privilege

44 IEEE RADARCONF 2021 7 SPECIAL EVENTS

45 IEEE RADARCONF 2021 7 SPECIAL EVENTS of working with Mike to fulfill one of his last wishes by establishing the IEEE AESS Michael Wicks Fund:

Each year - Graduate Students who are lead authors on a paper in the area of radar signal processing accepted for presentation during the annual IEEE Radar Conference can receive, if selected, travel support to attend the conference and present their research.

We are grateful to Mike for sharing his time, talent and treasure with the IEEE Radar Community and sadden that he passed away before we had the chance to use the Fund for the first time.

Join Mike in supporting this worthy endeavor and help perpetuate his memory. Make a gift to the IEEE Foundation at:

www.ieeefoundation.org/Wicks

7.4 Young Professionals Panel Session: A Spectrum of Careers in Radar

When: Wednesday May 12, 1:20 - 2:20 Where: Virtual Room A

The field of radar offers a wide range of exciting career opportunities. Academia, industry, government and the military all contribute to radar advancement across a diverse set of technical areas, as exemplified by the many sessions at the 2021 IEEE Radar Conference. This session features panelists from various aspects of the radar profession who will illustrate a few possible career trajectories that span technical execution, business development, and project management. They will share their perspectives on backgrounds, skills and practices that have proven valuable, as well as what excites them about the field. Whether you’re a young engineer, new to the field,or just curious about some paths others have taken, please join us for a discussion about careers in radar.

Chris Baker has been active in radar research for over thirty-five years, has published widely and has won numerous awards. He led the government research teams responsible for the development of the Sentinel and MSTAR systems in service with the UK Army and Air Force. Chris is working with colleagues at the UK National Quantum Technology Hub for Sensors and Metrology to lead an investigation into quantum radar.

He is co-author of several books including the best-selling “Stimson’s introduction to airborne radar”, a foundational teaching text. He is an enthusias- tic communicator on the theme of testing and gives frequent talks to various groups at both the local and national level.

He has received major grants from awarding bodies and industrial companies from all over the world. These grants have supported research, often with a strong applications theme. Chris also provides consultancy and has been engaged by the UK and overseas governments to provide reviews and technical advice.

46 IEEE RADARCONF 2021 7 SPECIAL EVENTS

Dr. Daniel Cook is with the Georgia Tech Research Institute. He is currently assigned to the Office of Naval Research where he serves as an IPA Program Officer in the Ocean Engineering Team in Code 32. He supports programs in the areas of sensing, signal processing, and machine learning as applied to autonomous undersea systems. Dan’s technical background includes 20 years of experience in underwater acoustics and synthetic aperture sonar, as well as synthetic aperture radar. His radar work concentrates on signal processing for coherent imaging and adaptive filtering with application to stripmap and spotlight SAR, interferometry, coherent change detection, and image quality assessment.

David Karnick is an Electronics Engineer in the Radar Division of the Naval Research Laboratory in Washington, DC. His primary focus is on the testing and evaluation of Electronic Protection techniques for the AN/SPY-6 radar. He also works on a wide-band target generator being used at multiple radar testing sites around the country. Prior to NRL he spent four years at a defense contractor in Northern Virginia working on novel RF sensors and signal processing techniques, primarily in the field of navigation. David earned his B.S. (2010) and M.S. (2011) in Electrical Engineering, both from Case Western Reserve University in Cleveland, OH.

7.5 Keynote Speaker and Awards Ceremony

When: Wednesday May 12, 4:20 Where: Virtual Room A

The keynote address will be given by Dr. Jeffrey Skolnick and career and student awards will be presented by IEEE.

Jeffrey Skolnick: MOATAI - An AI Based Approach to Predict Disease Drivers, Drug Efficacy and its Application to COVID-19y

Despite tremendous advancements in biology over the past two decades, the success rates of drug discovery remain modest. Of drugs undergoing FDA clinical trials, despite heroic efforts and tremendous resources, 6/7 fail safety and 3/4 fail efficacy; tellingly, 97% of cancer related drug discovery efforts are unsuccessful. Possibly, these failures are symptomatic of significant gaps in understanding of the interrelationship between a disease, the key mode of action proteins (MOA) that cause it, the subset of MOA proteins, the protein drivers, which when targeted by drugs effectively treat the disease, and off-target interactions often responsible for toxicity. To address these major issues, the key ideas behind a novel AI-based methodology, MOATAI, which can successfully predict disease-protein-pathway-drug relationships are described. Application of MOATAI to COVID-19 successfully predicts the majority of its complications including respiratory failure, pul- monary embolisms, strokes as well loss of sense of smell/taste, unusual neurological symptoms, cytokine storm, and blood clots. Then, repurposed FDA-approved drugs to combat COVID-19’s clinical manifestations are predicted. This provides a set of high confidence drugs possibly useful in treating COVID-19’s severe adverse events including others such as loss of taste/smell.

47 IEEE RADARCONF 2021 7 SPECIAL EVENTS

Jeffrey Skolnick is a Regents’ Professor and the Director of the Center for the Study of Systems Biology in the School of Biological Sciences at the Georgia Institute of Technology. He is also the Mary and Maisie Gibson Chair in Com- putational Systems Biology and a Georgia Research Alliance Eminent Scholar in Computational Systems Biology. He attended graduate school in Chemistry at Yale, receiving a Ph.D. in Chemistry. He then held a postdoctoral fellowship at Bell Laboratories. Next, he joined the faculty of the Chemistry Department at Louisiana State University, Baton Rouge. Then, he moved to Washington Uni- versity. Following his emerging interest in biology, he joined the Department of Molecular Biology of the Scripps Research Institute, where he held the rank of Professor. Among his awards are the Southeastern Universities Research Association (SURA) Distinguished Scientist Award, the Sigma Xi Sustained Research Award, and an Alfred P. Sloan Research Fellowship. He is a Fellow of the AAAS, the Biophysical Society, and the St. Louis Academy of Science. He is the author of over 390 publications, has an h-index of 88 and has served on 16 editorial boards. Dr. Skolnick’s current research interests are in computational biology and bioinformatics. He has developed AI based approaches to predict disease mode of action proteins, drug efficacy and side effects, and a non Mendelian approach to precision medicine. He has applied these tools to aging, cancer and chronic fatigue syndrome and cancer metabolomics. He has also done substantial research on the possible origins of the biochemistry of life.

7.6 Exhibitor Bingo Awards

When: Thursday May 13, 1:20 - 2:20 Where: Virtual Room A

The 2021 IEEE Radar Conference features an exciting range of exhibitors, each with a virtual booth that includes company information and provides opportunities to connect with company represen- tatives. Within the virtual booth, attendees can set an appointment with a particular representative or can start a chat with a representative who is actively working the booth. Our online Exhibitor Bingo Event, which starts Tuesday and continues through Thursday morning, gives participants the opportunity to visit each Exhibitor’s virtual booth to earn prizes including books and cash! Winners will be announced during lunch on Thursday, May 13th.

7.7 Government/Industry Panel

When: Thursday May 13, 4:00 - 5:50 Where: Virtual Room A

Dr. Tim Grayson, Director, DARPA Strategic Technology Officer, will chair a 90-minute industry leadership panel discussing a potentially broad range of topics of interest to include:

• DARPA’s MOSAIC initiative and its similarity to the emerging Internet of Things including autonomous transportation and the commercial radars which will make this a reality

48 IEEE RADARCONF 2021 7 SPECIAL EVENTS

• Commercial proliferated low-earth orbit (pLEO) constellations and its impact on earth sensing

• Workforce trends and how the emerging radar market expansion are impacted by and will impact these trends

The panel consists of senior leaders from Lockheed Martin, Systems & Technology Research (STR), and Georgia Tech Research Institute (GTRI). Biographies of the panelists follow.

Dr. Timothy P. Grayson Moderator is the Director of the Strategic Technology Office (STO) at DARPA. In this role, he leads the office in development ofbreak- through technologies to enable warfighters to field, operate, and adapt distributed, joint, multi-domain combat capabilities at continuous speed. These technologies include sensing, communications, and electronic warfare technology and the supporting tools and decision aids needed to compose, integrate, and operate complex combat architectures.

Dr. Grayson came to STO in 2018 from a varied career in government and industry. Most recently he was the founder and president of Fortitude Mission Research LLC, a consulting company specializing in organizational and operational strategy development and technology analysis related to defense, security, and intelligence. His primary client was DARPA, and in this role, he provided direct support to the Deputy Secretary of Defense’s Mod- ernization Study, of which DARPA was the lead in the fall of 2017. Dr. Grayson helped spearhead the project that resulted in “A Blueprint for Winning,” a framework for how to modernize the Depart- ment for the 21st century.

Dr. Grayson has extensive government experience. He spent several years as a senior intelligence officer with the Central Intelligence Agency (CIA) in the Directorate of Science and Technology and culminating in a tour at the Office of the Director of National Intelligence. Prior to CIA, Dr. Grayson was a program manager and senior scientist at DARPA. He initiated new programs in space situation awareness and networked sensing and also managed DARPA’s quick reaction program portfolio, successfully deploying technology to Afghanistan during the early days of Operation Enduring Freedom

Dr. Grayson holds a Ph.D. in Physics from University of Rochester, where he specialized in quantum optics, and a B.S. in Physics from University of Dayton with minors in mathematics and computer science.

Dr. Mark McClure currently serves as Executive Vice President and Chief Tech- nology Officer at Systems & Technology Research (STR), a company heco- founded in 2011. At STR, Dr. McClure leads scientist and engineers in the development of national security applications of advanced cyber, sensor, and data analytics technology.

Prior to STR, Dr. McClure was Vice President and Director of Sensors and PED Technology at SAIC (formally SET Corp) where he led and directed programs involving millimeter wave radar, multi-sensor (radar/EO/IR) data explosion, electromagnetic modeling and simulation, and real-time signal processing.

49 IEEE RADARCONF 2021 7 SPECIAL EVENTS

Dr. McClure was a Program Manager in DARPA IXO and IPTO from 2005 to 2009 where he managed a portfolio of radar development and sensor data exploitation programs. Prior to joining DARPA, Dr. McClure was Associate Leader of the RF Array Systems Group at MIT Lincoln Laboratory where he led development programs in advanced radar systems and technology.

Dr. McClure was with the Raytheon Company, Missile Systems Division from 1987 to 1995 where he was engaged in the development of advanced ground and airborne radar system electronics and signal processing algorithms.

Dr. McClure is a member of the DARPA Microsystems Exploratory Council (MEC) and the Duke University ECE Industry Advisory Board.

Dr. McClure received his Ph.D. from Duke University, his M.S. from Polytechnic University (now NYU), and his B.S. from Northeastern University — all in Electrical Engineering.

Dr. William Melvin is Deputy Director for Research at the Georgia Tech Re- search Institute (GTRI), Director of the Sensors and Intelligent Systems Direc- torate at GTRI, a University System of Georgia Regents’ Researcher, and an Ad- junct Professor in Georgia Tech’s Electrical and Computer Engineering Depart- ment. His research interests include all aspects of sensor technology development, systems engineering, developmental planning, autonomous and intelligent systems and machine learning. He has authored numerous papers and reports in his areas of expertise and holds three US patents on adaptive sensor technology. He is the co-editor of two of the three volumes of the popular Principles of Modern Radar book series.

Among his distinctions, Dr. Melvin is the recipient of a Commander’s Public Service Award from the Secretary of the Air Force for Acquisition in 2019, the 2014 IEEE Warren White Award, the 2006 IEEE AESS Young Engineer of the Year Award, the 2003 US Air Force Research Laboratory Reservist of the Year Award, and the 2002 US Air Force Materiel Command Engineering and Technical Man- agement Reservist of the Year Award. He was chosen as an IEEE Fellow for his contributions to adaptive radar technology, and is also a Fellow of the Military Sensing Symposium (MSS). He served on the USAF Science Advisory Board, served on the Board on Army Science and Technology through the National Academy of Science, served on the Air Force Studies Board on Developmental Planning organized through the National Academy of Science, and has served on other committees sponsored by the National Research Council. Additionally, Dr. Melvin chaired a Red Team review of the USAF Distributed Common Ground Station (DCGS) in 2012, served as a panel member for the EW ECCT S&T Workshop in 2018, was a contributing member of the AFRL Commander’s Spec- trum Warfare and Communications Expert Panel in 2018, served on a White House panel looking at issues around 5G deployment and military electronic system co-existence for the President’s Science Advisor in early 2020, and served on the White House/OSTP-DoD American Mid-Band Ini- tiative Team (AMBIT) as a technical consultant in 2020. He has served in a number of technical advisory roles on US Government acquisition programs.

Dr. Melvin received the Ph.D. in Electrical Engineering from Lehigh University, as well as the MSEE and BSEE degrees (with high honors) from this same institution, respectively.

50 IEEE RADARCONF 2021 7 SPECIAL EVENTS

Dr. Paul Stern is currently the Joint All-Domain Operations Chief Architect for the Lockheed Martin Corporation, the Chief Solutions Architect for Lockheed Martin Space, and a Lockheed Martin Senior Fellow. He has mission breadth and technical depth across the Space, Air and Surface C4ISR domains, including Space Security. His leadership, architecture and analysis skills, has enabled him to impact multiple Lockheed Martin Business Areas in technical strategy, capability roadmap development, technical program execution, program capture, and research facets. Prior to joining LM Astronautics in January 2000, Dr. Stern served for 3 years as a PI on DARPA IXO high performance computing initiatives. He received his Bachelor, Masters, and Doctorate degrees in Aerospace Engineering from the University of Colorado, Boulder. After joining LM, he took on increasing levels of respon- sibilities including the roles of Chief Architect/Chief Engineer on both large programs and major captures. As a LM Senior Fellow, he engaged with both Program and Engineering senior leadership across LM Space, Aeronautics, Missiles and Fire Control (MFC), and Rotary and Mission Systems (RMS) Business Areas to share concepts and architectural constructs to improve our technical and mission performance. His expertise in development/sustainment program execution covers the entire engineering lifecycle and includes all fundamental aspects of technical execution, technical assessments, program performance management, quantitative management, and risk/opportunity management. His technical specialty areas include mission management, command/control, mission data processing, and data fusion across multiple mission architectures and sensor phe- nomenologies. He has also has chaired and served on significant technical reviews and independent assessments across multiple Business Areas.

51 IEEE RADARCONF 2021 8 IEEE AWARDS

8 IEEE Awards

IEEE Radar Conference 2021 congratulates the following individuals for outstanding achievements in their field.

8.1 Robert T. Hill Best Dissertation Award

Francesca Filippini

The Robert T. Hill Best Dissertation Award is an annual AESS award to recognize candidates that have recently received a Ph.D. degree and have written an outstanding Ph.D. dissertation in the field of interest of the Aerospace and Electronic Systems Society. Its purpose is to grant international recognition for the most outstanding Ph.D. dissertation by an AESS member.1

Francesca Filippini is the recipient of this year’s Robert T. Hill Dissertation Award in recognition of her PhD dissertation at Sapienza University of Rome: “Multichannel Passive Radar Systems: Signal Processing Techniques and Design Strategies.”

Francesca Filippini received the M.Sc. degree (cum Laude) in Communications Engineering and the Ph.D. degree (cum Laude) in Radar and Remote Sensing from Sapienza University of Rome, Rome, Italy, in 2016 and 2020, respectively. From January to May 2016, she carried out her Master thesis research in the Passive Radar and Antijamming Techniques Department at Fraunhofer-Institut FHR, Wachtberg, Germany. She is currently a Postdoctoral Researcher at the Department of Information Engineering, Electronics and Telecommunications at Sapienza University of Rome. Dr. Filippini received the 2020 GTTI award for the best Ph.D. thesis defended at an Italian university in the areas of Communication Technologies. She was a recipient of the 2018 Premium Award for the Best Paper in IET Radar, Sonar and Navigation, the Best Paper Award at the 2019 International Radar Conference, the Second Best Student Paper Award at the 2018 IEEE Radar Conference, and the Best Paper Award at the 2017 GTTI Workshop on Radar and Remote Sensing. Since 2019, she has been serving in the IEEE Aerospace and Electronic System Society (AESS) Board of Governors, currently as Secretary and Co-Chair of the AESS Professional Networking and Mentoring Program.

1https://ieee-aess.org/membership/awards/robert-hill-award

52 IEEE RADARCONF 2021 8 IEEE AWARDS

8.2 Fred Nathanson Memorial Radar Award

Alexander Charlish

This award is in honor of the late Fred Nathanson and is sponsored by the IEEE Radar Systems Panel of the AES Society. The purpose of this award is to grant international recognition for outstanding contributions to the radar art. The goals of the Radar Systems Panel in granting this award are to encourage individual effort and to foster increased participation by developing radar engineers.2

Alexander Charlish is the recipient of this year’s Fred Nathanson Memorial Radar Award for contributions to radar resource management and cognitive radar.

Alexander Charlish obtained his M.Eng. degree from the University of Nottingham in 2006 and received his Ph.D. degree from University College London in 2011 on the topic of multifunction radar resources management. In 2011, he joined the Sensor Data and Information Fusion (SDF) De- partment at the Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE), where he now leads the Sensor and Resources Management Group. In this role, he leads a group of scientists conducting research on intelligent sensing with a focus on cognitive radar and resources management for sensor systems. Additionally, he is a visiting lecturer at RWTH Aachen University. He is currently an Associate Editor for Radar Systems for IEEE Transactions on Aerospace and Electronic Systems, and a Subject Editor for Radar, Sonar and Navigation for IET Electronic Letters. He is a senior member of the IEEE, a member of the IEEE AESS Board of Governors for the term 2021 – 2023, and is currently vice-chair of the IEEE AESS Radar Systems Panel. He is also active in the NATO community, where he currently co-chairs the Cognitive Radar Research Task Group. He received the NATO SET Panel Excellence Award in 2019 and the NATO SET Panel Early Career Award in 2020.

2https://ieee-aess.org/membership/awards/nathanson

53 IEEE RADARCONF 2021 8 IEEE AWARDS

8.3 AES Society Pioneer Award

Alfonso Farina

The Pioneer Award has been given annually since 1949 to an individual or team for “contributions significant to bringing into being systems that are still in existence today.” These systemsfall within the specific areas of interest to the society, that is, electronic or aerospace systems.The contributions for which the award is bestowed are to have been made at least twenty (20) years prior to the year of the award, to ensure proper historical perspective.3

Alfonso Farina is the recipient of this year’s AES Society Pioneer Award for pioneering contributions to the analysis, design, development, and experimentation of digital-based adaptive radar systems.

Alfonso Farina (FREng, FIET, LFIEEE, Fellow of EURASIP, EurASc) received the Doctoral – Laurea - degree in electronic engineering from the University of Rome, Rome, Italy, in 1973. In 1974, he joined SELENIA S.P.A., then Selex ES, where he became the Director of the Analysis of Integrated Systems Unit and, subsequently, the Director of Engineering of the Large Business Systems Di- vision. In 2012, he was the Senior VP and the Chief Technology Officer (CTO) of the Company, reporting directly to the President. From 2013 to 2014, he was a Senior Advisor to the CTO. He retired in October 2014. From 1979 to 1985, he was also a Professor of Radar Techniques with the University of Naples, Italy. He is currently a Visiting Professor with the Department of Electronic and Electrical Engineering at University College London and with the Centre of Electronic Warfare, Information and Cyber at Cranfield University, a Distinguished Lecturer of the IEEE Aerospace and Electronic Systems Society and a Distinguished Industry Lecturer for the IEEE Signal Processing Society (Jan 2018-Dec 2019). He is a Consultant to Leonardo S.p.A. “Land & Naval Defence Elec- tronics Division” (Rome). He has authored or co-authored more than 800 peer-reviewed technical papers and books and monographs (published worldwide), some of them also translated in to Rus- sian and Chinese. He received the IEEE Dennis J. Picard Medal for Radar Technologies and Appli- cations for “Continuous, Innovative, Theoretical, and Practical Contributions to Radar Systems and Adaptive Signal Processing Techniques” (2010). IEEE Signal Processing Society Industrial Leader Award (2018). Christian Hülsmeyer Award from the German Institute of Navigation (DGON) (2019). IEEE AESS Pioneer Award (2020). Honorary chair of IEEE RadarConf 2020, Florence.

3https://ieee-aess.org/membership/awards/pioneer

54 IEEE RADARCONF 2021 8 IEEE AWARDS

8.4 AESS Industrial Innovation Award

Miroslav N. Velev

The AESS Industrial Innovation Award recognizes an individual or team at any level who were industry employees whose technical contributions have resulted in significant advances in integrated electronic systems and large-scale integrated interoperable systems within the scope of the Soci- ety.4

Miroslav N. Velev is the recipient of this year’s AESS Industrial Innovation Award for contributions to formal verification of microprocessors for aerospace applications and Boolean Satisfiability for SAT solvers.

Miroslav N. Velev received B.S. & M.S. in Electrical Engineering, and B.S. in Economics from Yale University in 1994, and Ph.D. in Electrical and Computer Engineering (ECE) from Carnegie Mellon University in 2004. In 2002 – 2003 he was Visiting Assistant Professor in the School of ECE at Georgia Tech. In 2005 he started Aries Design Automation, where he is President, and is leading R&D on formal verification, Boolean Satisfiability (SAT), and related topics.

His research contributions include: the property of Positive Equality, where suitable abstractions in designing pipelined/superscalar/VLIW microprocessors result in a special structure of the cor- rectness formulas that can be exploited automatically in a formal verification tool to produce at least 5 orders of magnitude speedup, and scalability for large and complex designs with minimal manual effort; block-level translation of Boolean formulas to Conjunctive Normal Form (CNF), the most widely used input format of SAT solvers, resulting in at least 2 orders of magnitude speedup, and an order of magnitude increase in capacity with any SAT solver; and hierarchical hybrid encod- ings for solving of Constraint Satisfaction Problems (CSPs) by efficient translation to equivalent SAT problems, producing up to 8 orders of magnitude speedup.

In the last 20 years, Dr. Velev has served: on the technical program committees of more than 400 conferences; on the organizing committees of more than 60 conferences; as session chair/orga- nizer of more than 100 sessions; as track/discipline chair at 20 conferences; on the best-paper- award committees of 11 conferences, and chaired 3 of them. He has served or serves on the editorial boards of 9 journals, as guest editor of 3 special journal issues on SAT, AI, and formal

4https://ieee-aess.org/membership/awards/industrial-innovation

55 IEEE RADARCONF 2021 8 IEEE AWARDS verification topics, and as Technical Program Chair of four prestigious conferences, including the 36th IEEE/AIAA Digital Avionics Systems Conference (DASC’17), 2017.

Dr. Velev received the EDAA Outstanding Dissertation Award, 2005, and the Franz Tuteur Memo- rial Prize for Most Outstanding Senior Project in Electrical Engineering, Yale University, 1994. He is: Fellow of the American Association for the Advancement of Science (AAAS), 2017; Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA), 2017; and Distinguished Member (Scientist) of the Association for Computing Machinery (ACM), 2014.

8.5 Warren White Award for Excellence in Radar Engineering

Daniel W. Bliss

Dana White Starr and Warren H. White established the Warren D. White Memorial Fund in 1999 and the Warren D. White Award to memorialize their father. The award is to recognize a radar engineer for outstanding achievements due to a major technical advance (or series of advances) in the art of radar engineering. The advance, significant, public, and well known, shall be evidenced by technical papers, inventions, presentations, or products.5

Daniel W. Bliss is the recipient of this year’s Warren White Award for Excellence in Radar Engineer- ing award For Contributions to Multiple-Input Multiple-Output Radar, Multiple-Function Sensing and Communications Systems, and Novel Small-Scale Radar Applications.

Prof. Daniel W. Bliss (bliss.asu.edu) is a Professor in the School of Electrical, Computer, and En- ergy Engineering at Arizona State University and a Fellow of the IEEE. He is also the Director of ASU’s Center for Wireless Information Systems and Computational Architectures (wisca.asu.edu). Dan received his Ph.D. and M.S. in Physics from the University of California at San Diego (1997 and 1995), and his B.S. in Electrical Engineering from ASU (1989). His current research focuses on advanced systems in the areas of radar, communications, precision positioning, advanced computational systems, and medical monitoring. Dan has been the principal investigator on numerous projects including sponsored programs with DARPA, ONR, Google, Airbus, and others. He is responsible for foundational work in electronic protection, adaptive multiple-input multiple-output

5https://ieee-aess.org/membership/awards/warren-white-award

56 IEEE RADARCONF 2021 8 IEEE AWARDS

(MIMO) radar, MIMO communications, distributed-coherent systems, and RF convergence. Be- fore moving to ASU, Dan was a Senior Member of the Technical Staff at MIT Lincoln Laboratory (1997-2012). Between his undergraduate and graduate degrees, Dan was employed by General Dynamics (1989-1993), where he designed avionics for the Atlas-Centaur launch vehicle and performed magnetic field optimization for high-energy particle-accelerator superconducting magnets. His doctoral work (1993-1997) was in the area of high-energy particle physics and lattice-gauge- theory calculations. Dan is a member of the IEEE AES Radar Systems Panel and is a member of the IEEE Signal Processing Magazine Editorial Board.

8.6 M. Barry Carlton Award

Farzad Hessar Sumit Roy Bo Li Athina P.Petropulu

The M. Barry Carlton Award is an annual award recognizing the best paper published in the Trans- actions on Aerospace and Electronic Systems. To help assess impact, nominations are limited to the papers published in calendar year four years before the award year. For example, the Carlton Award made in 2020 was to a paper published in the Transactions in 2016.

The award was established in 1957 after the early death of M. Barry Carlton in an air accident the year before. M. Barry Carlton’s friends established the award as a means to honor a man who had dedicated much of his life to promoting the reliability of communications equipment, especially that relating to air transportation. It is one of the IEEE’s oldest awards and supports a wonderful tradition of excellence.6

Farzad Hessar and Sumit Roy are the recipient of this year’s M. Barry Carlton Award for their paper entitled “Spectrum Sharing Between a Surveillance Radar and Secondary Wi-Fi Networks”. Addition- ally, Bo Li and Athina P.Petropulu are recipients of the award for their paper entitled “Joint Transmit Designs for Coexistence of MIMO Wireless Communications and Sparse Sensing Radars in Clutter”.

6https://ieee-aess.org/membership/awards/m-barry-carlton-award

57 IEEE RADARCONF 2021 8 IEEE AWARDS

8.7 IEEE Dennis J. Picard Medal for Radar Technologies and Applications

Simon Haykin

The IEEE Dennis J. Picard Medal for Radar Technologies and Applications was established in 1999, in honor of Dennis J. Picard, whose lifetime of work at Raytheon Company helped make them a leader in tactical missile systems. In the evaluation process, the following criteria are considered: field leadership, contribution, originality, breadth, inventive value, publications, other achievements, society activities, honors, duration, and overall strength of the nomination.7

Simon Haykin is the recipient of this year’s IEEE Dennis J. Picard Medal for Radar Technologies and Applications for contributions to the development of the theory and practice of radar, especially cognitive radar and adaptive filtering.

Simon Haykin received his B.Sc. (First-class Honours), Ph.D., and D.Sc., all in Electrical Engineering from the University of Birmingham, England. He is a Fellow of the Royal Society of Canada, and a Fellow of the Institute of Electrical and Electronics Engineers. He is the recipient of the Henry Booker Gold Medal from URSI, 2002, the Honorary Degree of Doctor of Technical Sciences from ETH Zentrum, Zurich, Switzerland, 1999, and many other medals and prizes.

He is a pioneer in adaptive signal-processing with emphasis on applications in radar and communications, an area of research which has occupied much of his professional life.

In the mid-1980s, he shifted the thrust of his research effort in the direction of Neural Computation, which was re-emerging at that time. All along, he had the vision of revisiting the fields of radar and communications from a brand new perspective. That vision became a reality in the early years of this century with the publication of two seminal journal papers:

“Cognitive Radio: Brain-empowered Wireless communications”, which appeared in IEEE J. Selected Areas in Communications, Feb. 2005.

“Cognitive Radar: A Way of the Future”, which appeared in the IEEE J. Signal Processing, Feb. 2006.

Cognitive Radio and Cognitive Radar are two important parts of a much wider and integrative field: Cognitive Dynamic Systems, research into which has become his passion.

7https://ieee-aess.org/membership/awards/ieee-dennis-j-picard-medal

58 IEEE RADARCONF 2021 8 IEEE AWARDS

8.8 AESS Engineering Scholarships

Alex Towfigh Minghui Sun

This scholarship recognizes students pursuing studies in Electrical Engineering at the undergraduate level, and systems engineering at the graduate level.8

8.9 AESS Fellows

The 2021 AESS Fellows are as follows:

1. Stefano Coraluppi for contributions to multi-sensor multi-target tracking

2. Michael Griffin for leadership in space infrastructure and Space Shuttle, International Space Station, Hubble and other missions

3. Robert Shin for leadership in electromagnetic modeling of radar systems and in micorwave remote sensing

4. Birsen Yazici for contributions to synthetic aperture radar and passive imaging

8http://ieee-aess.org/membership/awards/aess-scholarship

59 IEEE RADARCONF 2021 8 IEEE AWARDS

8.10 Harry Mimno Best Paper Award

Sevgi Zubeyde Gurbuz Hugh D. Griffiths Alexander Charlish

Muralidhar Rangaswamy Maria Sabrina Greco Kristine Bell

Sevgi Zubeyde Gurbuz, Hugh D. Griffiths, Alexander Charlish, Muralidhar Rangaswamy, Maria Sabrina Greco, and Kristine Bell are the recipient of the Harry Mimno Best Paper Award for the paper entitled ”An Overview of Cognitive Radar: Past, Present, and Future” published in IEEE Aerospace and Electronic Systems Magazine, Vol. 34, No. 12, December 2019.

60 IEEE RADARCONF 2021 8 IEEE AWARDS

8.11 Radar Challenge

The Radar Challenge is a series of events co-hosted with radar conferences that enables participants to experience the magic of radar in a personal and tangible way. The events encourage participants to experiment with self-engineered “home-brew” radar. The goal is to build a community of radar builders that collectively explore the art of the possible. For 2021, the Virtual Radar Challenge is for conference participants to prepare novel, impactful, educational, appealing tutorial videos themed on building and experimenting with home-brew radars. The videos will be made available to conference participants during the conference for public vote, and also will be judged by a panel of radar experts. The winning videos will be awarded prizes.

Special thanks to Anteral, Texas Instruments, Mathworks, and the IEEE Atlanta AESS/GRSS Joint Chapter for their contributions and support of the radar challenge.

By Anteral

61 IEEE RADARCONF 2021 9 STUDENT PAPER FINALISTS

9 Student Paper Finalists

IEEE Radarconf 2021 student paper finalists are recognized for their exceptional content and technical contributions. Finalists are invited to present their work remotely at the conference to a panel of judges from industry and academia. The top three students will receive a cash award and recognition during the banquet awards ceremony on Wednesday, May 12, 2021.

1. Online Multi-Target Tracking for Pedestrian by Fusion of Millimeter Wave Radar and Vision Fucheng Cui, Yuying Song, Jingxuan Wu, Zhouzhen Xie, Chunyi Song, Zhiwei Xu, Kai Ding

2. High-Resolution Drone-Borne SAR Using Off-the-Shelf High-Frequency Radars Ali Bekar, Michail Antoniou, Christopher J. Baker

3. Cognitive Optimization of Sparse Array Transceiver for MIMO Radar Beamforming Weitong Zhai, Xiangrong Wang, Syed A. Hamza, Moeness G. Amin

4. Computationally Efficient Joint-Domain Clutter Cancellation for Waveform-Agile Radar Christian Jones, Brandon Ravenscroft, James Vogel, Suzanne Shontz, Thomas Higgins, Kevin Wagner, Shannon Blunt

5. Multi-Frequency RF Sensor Data Adaptation for Motion Recognition with Multi-Modal Deep Learning Mohammed Rahman, Sevgi Gurbuz

62 IEEE RADARCONF 2021 10 RADAR SYSTEMS PANEL MEMBERS

10 Radar Systems Panel Members

Raviraj Adve University of Toronto Laura Anitori Netherlands Organisation for Applied Scientific Research (TNO) Augusto Aubry University of Naples Federico II Stéphanie Bidon ISAE-SUPAERO Igal Bilik Ben Gurion University Kristin Bing Georgia Tech Research Institute Dan Bliss Arizona State University Alexander Charlish Fraunhofer FKIE Fabiola Colone Sapienza University of Rome William Correll Maxar Technologies Antonio De Maio University of Naples Federico II Jacqueline Fairley Georgia Tech Research Institute J. Scott Goldstein SAIC Nathan Goodman University of Oklahoma Martie Goulding MDA Systems Ltd. Sevgi Z. Gurbuz University of Alabama Julie Jackson Air Force Institute of Technology Peter Knott Fraunhofer FHR Visa Koivunen Aalto University Krzysztof Kulpa Warsaw University of Technology Mateusz Malanowski Warsaw University of Technology Anthony Martone US Army Research Laboratory Marco Martorella University of Pisa Justin Metcalf University of Oklahoma Willie Nel Council for Scientific and Industrial Research Jennifer Palmer Georgia Tech Research Institute Michael Picciolo ENSCO Christ Richmond Arizona State University Matthew Ritchie University College London Frank Robey MIT Lincoln Laboratory Luke Rosenberg Defence Science and Technology Group (DST) Stéphane Saillant ONERA Piotr Samczynski Warsaw University of Technology K. James Sangston Georgia Tech Research Institute Aaron Shackelford Naval Research Laboratory Graeme Smith JHU Applied Physics Laboratory John Stralka Northrop Grumman Lars Ulander Swedish Defence Research Agency (FOI) Faruk Uysal Netherlands Organisation for Applied Scientific Research (TNO) Jennifer Watson MIT Lincoln Laboratory Xiaopeng Yang Beijing Institute of Technology Mark Yeary University of Oklahoma

63 IEEE RADARCONF 2021 11 CORPORATE PATRONS

11 Corporate Patrons

IEEE Radarconf 2021 thanks the following event patrons for their support.

Diamond

Gold

Silver

Bronze

64 IEEE RADARCONF 2021 12 EXHIBITORS

12 Exhibitors

The 2021 IEEE Radar Conference is pleased to welcome the following exhibitors.

65 IEEE RADARCONF 2021 13 TUTORIALS

13 Tutorials

We are pleased to present a wide selection of tutorials from distinguished academics and professionals around the globe. Please consult the website for more information on access and timing.

13.1 Overview

Monday Morning (5/10) Monday Afternoon (5/10) Friday Morning (5/14) 08:00-12:00 (UTC-4) 13:00-17:00 (UTC-4) 08:00-12:00 (UTC-4)

MA-1: Recent MP-1: Passive radar – FA-1: Virtual RF Developments in Maritime from target detection to Environments to Support Radar Detection imaging Advanced Radar Mode Development MA-2: Introduction to MP-2: Advanced Inverse Airborne Ground-Moving Synthetic Aperture Radar FA-2: Deep Learning for Target Indicator (GMTI) Imaging Radio Frequency Radar Automatic Target MP-3: Digital Array Radar Recognition MA-3: Introduction to Automotive Radars MP-4: Deep Learning for FA-3: Multi-function Radar Radar Systems with Resources Management MA-4: Convex MATLAB Optimization for Adaptive FA-4: Micro-Doppler Radar MP-5: Efficient Spectral Signatures: Principles, Access for Radar and Analysis and applications MA-5: Radar tracking: a Communications long-standing cooperation FA-5: Terahertz and between industry and Sub-Terahertz Automotive academia Radar: Emerging Technologies and MA-6: Analytic Challenges Combinatorics for Multi-Object Tracking FA-6: Bistatic and Multistatic Radar Imaging

66 IEEE RADARCONF 2021 13 TUTORIALS

13.2 Recent Developments in Maritime Radar Detection [MA-1]

Instructors

• Dr. Luke Rosenberg, Defence Science and Technology Group • Prof. Simon Watts, University College London

Abstract

Traditional maritime radar is based on non‐coherent detection, mainly due to the complexities of implementing coherent detectors in sea clutter. Over the past decade, there has been significant new research into the characterization and modelling of sea clutter and how to improve maritime target detection. The use of models has also led to techniques for predicting the performance of many new radar detection schemes. This tutorial will include a comprehensive coverage of new research in three key areas. The first is sea clutter modelling for both monostatic and active bistatic radar systems. The second area looks at a number of detection schemes, which have been proposed for detection of targets in sea clutter. These include both non‐coherent techniques based on constant false alarm rate (CFAR) schemes, coherent single and multichannel techniques and approaches based on time‐frequency analysis and sparse signal separation. The final part of the tutorial links these two areas by showing how sea clutter models can be used to determine the expected detection performance of both non‐coherent and coherent detection schemes.

Instructor Biographies

Prof. Simon Watts graduated from the University of Oxford in 1971, obtained an MSc and DSc from the University of Birmingham in 1972 and 2013, respectively, and a PhD from the CNAA in 1987. He was deputy Scientific Director and Technical Fellow in Thales UK until 2013 and is a Visiting Professor in the department of Electronic and Electrical Engineering at University College London. He joined Thales (then EMI Electronics) in 1967 and since then has worked on a wide range of radar and EW projects, with a particular research interest in maritime radar and sea clutter. He is author and co‐author of over 60 journal and conference papers, a book on sea clutter and several patents. He was chair of the international radar conference RADAR‐97 in Edinburgh UK. Professor Watts received the IEE JJ Thomson Premium Award in 1987 and the IEE Mountbatten Premium Award in 1991. He has served as an Associate Editor for Radar for the IEEE Transactions AES and is a member of the Editorial Board of IET Radar, Sonar & Navigation. He was appointed MBE in 1996 for services to the UK defense industry and is a Fellow of the Royal Academy of Engineering, Fellow of the IET, Fellow of the IMA and Fellow of the IEEE.

67 IEEE RADARCONF 2021 13 TUTORIALS

Dr. Luke Rosenberg received the Bachelor of Electrical and Electronic Engineer- ing in 1999, the Masters in Signal and Information Processing in 2001 and the Ph.D. in 2007, all from the University of Adelaide, Australia. In 2016, he completed the Graduate Program in Scientific Leadership at the University of Mel- bourne, Australia. He is currently a Discipline Lead for Maritime Airborne Radar in the Defence Science and Technology Group, Australia. His work covers the areas of radar image formation, adaptive filtering, detection theory, and radar and clutter modelling. He is an adjunct Associate Professor at the University of Adelaide, and in 2014 spent 12 months at the U.S. Naval Research Laboratory (NRL) working on algorithms for focusing moving scatterers in synthetic aperture radar imagery. Dr. Rosenberg has jointly received the best paper awards at international radar conferences in 2014 and 2015 and has presented a number of tutorials at the IEEE American (national) and international radar conferences. In 2016, he received the prestigious Defence Science and Technology Achievement Award for Science and Engineering Excellence and in 2017, the NRL ARPAD award with colleagues from the NRL, and in 2018, the IEEE AESS Fred Nathanson award for ‘Fundamental Experimental and Theoretical Work in Characterizing Radar Sea Clutter‘.

13.3 Introduction to Airborne Ground-Moving Target Indicator Radar [MA-2]

Instructor

• Dr. Armin Doerry, Sandia National Laboratories

Abstract

Airborne Ground-Moving Target Indicator (GMTI) is a radar mode that detects and discriminates moving ground targets, such as vehicles and dismounts. This is an important Intelligence, Surveil- lance, and Reconnaissance (ISR) tool particularly for the military and intelligence communities, but also with application in the civilian and government communities. We will discuss the physical concepts, processing, performance, features, and exploitation modes that make GMTI radar work, and be useful. We will focus on the qualitative significance of the relevant mathematics rather than dry derivations, with liberal use of example GMTI data and other processing products to illustrate the concepts discussed.

The presentation will be given as four distinct modules.

1. Introduction and basic GMTI processing, including basic detection theory. We will focus on airborne pulse-Doppler systems. Basic data models will be developed, and several processing algorithms will be illustrated and compared. These include a simple range-Doppler algorithm, as well as keystone processing enhancements. Also included will be Constant False Alarm Rate (CFAR) target detection.

2. GMTI performance prediction and the radar equation. The radar equation for GMTI will be developed and explored in some detail to illustrate how GMTI operating parameters can be traded for performance. Target statistics will be presented for vehicles and dismounts, including Swerling models. Minimum Detectable Velocity (MDV) will be discussed.

68 IEEE RADARCONF 2021 13 TUTORIALS

3. Clutter mitigation techniques. Multichannel processing algorithms will be discussed like Dis- placed Phase Center Antenna (DPCA) techniques, Along-Track Interferometry (ATI), and basic Space-Time Adaptive Processing (STAP). Note that this is not a STAP course, but rather focused on the larger system for which STAP may be an optional component.

4. Ancillary topics. Several ancillary topics will be discussed, including, but not limited to, antenna design requirements, geometric effects, micro-Doppler, target tracking, the scope of moving targets, vibrometry, cross-polarization effects, and VideoSAR with shadow detection/tracking.

Instructor Biography

Dr. Armin Doerry is a Distinguished Member of Technical Staff in the ISR Mis- sion Engineering Department of Sandia National Laboratories. He holds a Ph.D. in Electrical Engineering from the University of New Mexico. He has worked in numerous aspects of airborne ISR and other radar systems’ analysis, design, and fabrication since 1987, and continues to do so today. He has taught Radar Signal Processing classes (and related topics) as an adjunct professor at the University of New Mexico, and has taught numerous seminars on SAR, GMTI, and other radar topics to government, military, industry, and academic groups.

13.4 Introduction to Automotive Radars [MA-3]

Instructor

• Dr. Igal Bilik, ECE Department, Ben Gurion University of the Negev, Israel

Abstract

Autonomous driving is one of the megatrends in the automotive industry, and a majority of car manufacturers are already introducing various levels of autonomy into commercially available vehicles. The main task of the sensing suite in autonomous vehicles is to provide the most reliable and dense information on the vehicular surroundings. Specifically, it is necessary to acquire information on drivable areas on the road and to port all objects above the road level as obstacles to be avoided. Thus, the sensors need to detect, localize, and classify a variety of typical objects, such as vehicles, pedestrians, poles, and guardrails. Comprehensive and accurate information on vehicle surroundings cannot be achieved by any single practical sensor. Therefore, all autonomous vehicles are typically equipped with multiple sensors of multiple modalities: radars, cameras, and lidars. Lidars are expensive and cameras are sensitive to illumination and weather conditions, have to be mounted behind an optically transparent surface, and do not provide direct range and velocity measurements. On the contrary, radars are robust to adverse weather conditions, are insensitive to lighting variations, provide long and accurate range measurements, and can be packaged behind optically nontransparent fascia. The uniqueness of automotive radar scenarios mandates the formulation and derivation of new signal processing approaches beyond classical military radar concepts. The reformulation of vehicular radar tasks, along with new performance requirements,

69 IEEE RADARCONF 2021 13 TUTORIALS provides an opportunity to develop innovative signal processing methods. As a result, Automotive Radars is active field of research in both industry and academia.

This Tutorial will first describe active safety and autonomous driving features and associated sensing challenges. Next it will overview technology trends and state advantages of available sensing modalities and describe automotive radar performance requirements. It will discuss propagation phenomena experienced by typical automotive radar and radar concepts that can address them. Next this tutorial will focus on the radar equation and the radar processing chain: range and Doppler measurement estimation, beamforming, detection, range and angle-of-arrival migration, tracking and clustering. Discussing modern automotive radars, the tutorial will describe MIMO radar methods. Finally, the automotive radar applications and advanced topics, such as interference mitigation, and sensor fusion will be discussed.

Instructor Biography

Igal Bilik received B.Sc., M.Sc., and Ph.D. degrees in electrical and computer engineering from the Ben- Gurion University of the Negev, Beer Sheva, Israel, in 1997, 2003, and 2006, respectively. During 2006– 2008, he was a postdoctoral research associate in the Department of Electrical and Computer Engineering at Duke University, Durham, NC. During 2008-2011, he has been an Assistant Professor in the Department of Electrical and Computer Engineering at the Uni- versity of Massachusetts, Dartmouth. During 2011-2019, he was a Staff Re- searcher at GM Advanced Technical Center, Israel, leading automotive radar technology development. Between 2019-2020 he was leading Smart Sensing and Vision Group where he led development of the state-of-art automotive sensing technologies: radar, lidar, vision and sensor fusion. Currently, Dr. Bilik is an Associate Professor in the Depart- ment of Electrical and Computer Engineering at the Ben Gurion University of the Negev. Dr. Bilik has more than 170 patent inventions, authored more than 60 peer-reviewed academic publications, received the Best Student Paper Awards at IEEE RADAR 2005 and IEEE RADAR 2006 Conferences, Student Paper Award in the 2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, and the GM Product Excellence Recognition in 2017.

13.5 Convex Optimization for Adaptive Radar [MA-4]

Instructors

• Prof. Vishal Monga, The Pennsylvania State University • Dr. Muralidhar Rangaswamy, Air Force Research Laboratory

Abstract

The main theme of the tutorial is to motivate, describe and illustrate the application of convex optimization principles for radar signal processing. The scope of the tutorial is to introduce a variety of optimization problems for adaptive radar processing, including disturbance covariance matrix estimation and waveform and receive filter design, encountered by real-world systems under challenging practical constraints. Incorporating the aforementioned constraints into an optimization framework often results in ill-posed problems where no unique solutions are available and no

70 IEEE RADARCONF 2021 13 TUTORIALS globally optimal solutions are guaranteed. The central thrust of the tutorial is to introduce novel optimization approaches to solve estimation, detection and waveform design problems core to modern radar signal processing that are complicated by a plethora of real-world effects arising from systems and environmental considerations. A key example of a resource constraint in this context is limited number of homogenous training samples for estimating statistics such as disturbance and clutter covariance. Phenomenology based constraints involve understanding and exploiting clutter rank in covariance estimation. On the other hand, hardware limitations force the inclusion of constant modulus constraint in waveform design. The tutorial will extensively employ the theory of convex and non-linear optimization, convex analysis, and approximation to expand on recent exciting progress in convex optimization for radar systems.

The tutorial is organized into three parts. The first part reviews modern radar STAP and motivates the need for algorithmic approaches rooted in constrained convex optimization. The second part focuses on the estimation of disturbance/clutter covariance: incorporating physically inspired constraints. Furthermore, we devote attention to imperfect knowledge of these constraints caused by a plethora of real world effects such as antenna errors, mutual coupling, internal clutter motion, and aircraft crabbing. Ameliorating solutions for these perturbations are also discussed in some detail. The third part delves into waveform optimization problems under constant modulus and similarity constraints. Also studied is waveform design that guarantees desirable beam patterns for MIMO radar and finally, the problem of waveform design under spectral interference constraints. Many new analytical results are introduced based on recently published work: some that generalize and extend known past work, and others that involve new optimization approaches altogether. For each practical radar problem considered, extensive experimental results will be shown illustrating the suitability of aforementioned optimization techniques for real-world conditions.

Instructor Biographies

Prof. Vishal Monga has been on the Electrical Engineering faculty at Penn State since Fall 2009, where he is currently a tenured Professor. From Oct 2005- July 2009 he was an imaging scientist with Xerox Research Labs. He has also been a visiting researcher at Microsoft Research in Redmond, WA and a visiting faculty at the University of Rochester. Prior to that, he received his PhD EE from the department of Electrical and Computer Engineering at the Univer- sity of Texas, Austin. Prof. Monga’s research has been recognized via the US National Science Foundation CAREER award. For his educational efforts, Dr. Monga received the 2016 Joel and Ruth Spira Teaching Excellence award. He has served on the editorial boards of the IEEE Transactions on Image Processing, IEEE Transac- tions on Circuits and Systems for Video Technology and the IEEE Signal Processing Letters. Dr. Monga is a Senior Member of the IEEE.

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Dr. Muralidhar Rangaswamy received the B.E. degree in Electronics Engineer- ing from Bangalore University, Bangalore, India in 1985 and the M.S. and Ph.D. degrees in Electrical Engineering from Syracuse University, Syracuse, NY, in 1992. He is presently employed as the Senior Advisor for Radar Research at the RF Exploitation Branch within the Sensors Directorate of the Air Force Research Laboratory (AFRL). Prior to this he has held industrial and academic appointments. He received the IEEE Warren White Radar Award in 2013, and the 2005 IEEE-AESS Fred Nathanson memorial outstanding young radar engineer award. He was elected as a Fellow of the IEEE in January 2006. Dr. Rangaswamy is a technical editor (Associate Editor-in-Chief) for the IEEE Transactions on Aerospace and Electronic Systems and was the technical program chair for the 2014 IEEE Radar Conference in Cincinnati, OH. Recently, he received the 2019 IEEE Dayton Section Fritz Russ memorial award for his contributions to cognitive radar signal processing, modeling and simulation, and architectures. He also received the 2019 Technical Cooperation Program ISTAR Basic Research Award.

13.6 Radar Tracking: A Long-Standing Cooperation Between Industry and Academia [MA-5]

Instructors

• Dr. Alfonso Farina Selex-ES (retired) • Dr. Giorgio Battistelli, Dipartimento di Ingegneria dell’Informazione, Università di Firenze, Italy • Dr. Luigi Chisci, Dipartimento di Ingegneria dell’Informazione, Università di Firenze, Italy

Abstract

The tutorial will describe the intertwined R&D activities, along several decades, between academia and industry in conceiving and implementing - on live radar systems - tracking algorithms for targets in civilian as well as defense and security applications.

We trace back from the alpha-beta adaptive filter to modern random set filters passing thru Kalman algorithm (in its many embodiments), Multiple Model filters, Multiple Hypothesis Tracking, Joint Probabilistic Data Association, Particle filter for nonlinear non Gaussian models. Fusion from heterogeneous collocated as well as non-collocated sensor data are also mentioned. Applications to land, naval and airborne sensors are considered. Active as well as passive radar experiences are overviewed. The description will be a balanced look to both mathematical aspects as well as practical implementation issues including mitigation of real life system limitations.

The aim is to show the long experience of fruitful cross-disciplinary cooperation between industry and universities especially in the field of signal and data processing in radar, where the advantage is that learning goes both ways in these relationships! It is shown that refined mathematical algorithms conceived by this cooperation can indeed either influence or even live in real systems. The tutorial will also provide a comprehensive overview of tools and methodologies for the design and evaluation of modern radar tracking systems.

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Instructor Biographies

Alfonso Farina (Fellow of EURASIP, FIEEE, FIET, FREng) received the degree in Electronic Engineering from the University of Rome (IT) in 1973. In 1974, he joined Selenia, then Selex ES, where he became Director of the Analysis of In- tegrated Systems Unit and subsequently Director of Engineering of the Large Business Systems Division. In 2012, he was Senior VP and Chief Technology Officer of the company, reporting directly to the President. From 2013 to2014, he was senior advisor to the CTO. He retired in October 2014. From 1979 to 1985, he was also professor of “Radar Techniques” at the University of Naples (IT). He is the author of more than 600 peer-reviewed technical publications and of books and monographs (published worldwide), some of them also translated into Russian and Chinese. Some of the most significant awards he’s received include: (2004) Leader of theteam that won the First Prize of the first edition of the Finmeccanica Award for Innovation Technology, out of more than 330 submitted projects by the Companies of Finmeccanica Group; (2005) Interna- tional Fellow of the Royal Academy of Engineering, U.K., and the fellowship was presented to him by HRH Prince Philip, the Duke of Edinburgh; (2010) IEEE Dennis J. Picard Medal for Radar Tech- nologies and Applications for “Continuous, Innovative, Theoretical, and Practical Contributions to Radar Systems and Adaptive Signal Processing Techniques”; (2012) Oscar Masi award for the AU- LOS® “green” radar by the Italian Industrial Research Association (AIRI); (2014) IET Achievement Medal for “Outstanding contributions to radar system design, signal, data and image processing, and data fusion”. He is a Visiting Professor at UCL, Dept. of Electronics, and Cranfield University.

Giorgio Battistelli received the degree in electronic engineering and the Ph.D. degree in robotics from the University of Genoa, Genoa, Italy, in 2000 and 2004, respectively. From 2004 to 2006, he was a Research Associate with the Dipar- timento di Informatica, Sistemistica e Telematica, University of Genoa. Since 2006 he has been with the University of Florence, Florence, Italy, where he is currently a Full Professor of automatic control with the Dipartimento di Ingegneria dell’Informazione. His current research interests include adaptive and learning systems, real-time control reconfiguration, linear and nonlinear estimation, hybrid systems, sensor networks, data fusion. Dr. Battistelli was a member of the editorial boards of the IFAC Journal Engineering Applications of Artificial Intelligence and of the IEEE Transactions on Neural Networks and Learning Systems. He is currently an Associate Editor of the IFAC Journal Nonlinear Analysis: Hybrid Systems, and a member of the conference editorial boards of IEEE Control Systems Society and the European Control Association.

Luigi Chisci received the degree in electrical engineering in 1984 from the Uni- versity of Florence and the Ph.D. in systems engineering in 1989 from the Uni- versity of Bologna. He is full professor at University of Florence. His educational and research career have been in the area of control and systems engineering. His research interests have spanned over: adaptive control and signal processing, algorithms and architectures for real-time control and signal processing, recursive identification, filtering and estimation, predictive control. His current interests concern networked estimation, multitarget multisensor tracking and distributed data fusion. He has coauthored over 180 papers of which about 80 on international journals. His research group has a long-standing collaboration with Al- fonso Farina’s group at Finmeccanica starting from 1994 on several topics including adaptive sig-

73 IEEE RADARCONF 2021 13 TUTORIALS nal processing, stochastic filtering, multitarget multisensor tracking and data fusion.

13.7 Analytic Combinatorics for Multi-Object Tracking [MA-6]

Instructors

• Roy L. Streit, Metron Inc. • R. Blair Angle, Metron Inc. • Dr. Murat Efe, Ankara University

Abstract

Exact solutions of many multitarget tracking problems have high computational complexity and are impractical for all but the smallest of problems. Practical implementations entail approximation. There is a bewildering variety of established trackers available, and practicing engineers and/or researchers often study them almost in isolation of each other without fully understanding what these trackers are about and how they are inter-related. One reason for this is that they have different combinatorial problems which are approached by explicitly enumerating the feasible solutions. The enumeration is usually a highly detailed, hard to understand accounting scheme specific to the filter, and the details cloud understanding the filter and make it hard to compare different filters.On the other hand, the analytic combinatorics approach presented in this tutorial avoids the heavy accounting burden and provides a solid tool to work with, namely the mixed derivative of multivariate calculus, which all engineers easily understand.

This tutorial is designed to facilitate understanding of the classical theory of Analytic Combina- torics (AC) and how to apply it to problems in multi-object tracking. AC is an economical technique for encoding combinatorial problems—without information loss—into the derivatives of a generating function (GF). Exact Bayesian filters derived from the GF avoid the heavy accounting burden required by traditional enumeration methods. Although AC is an established mathematical field, it is not widely known in either the academic engineering community or the practicing data fusion/ tracking community. This tutorial lays the groundwork for understanding the AC method, starting with the GF for the classical Bayes-Markov filter. From this cornerstone, we derive many established filters (e.g., PDA, JPDA, JIPDA, PHD, CPHD, MultiBernoulli, MHT) with simplicity, economy, and insight. We also show how to use the saddle point method (method of stationary phase) to find low complexity approximations of probability distributions and summary statistics.

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Instructor Biographies

Roy Streit Senior Scientist, Metron, Reston, Virginia, and Professor (Adjunct) of Electrical and Computer Engineering, University of Massachusetts– Dart- mouth. IEEE Fellow. IEEE AESS Board of Governors, 2016-18. President, ISIF, 2012. Research interests include multi-target tracking, multisensor data fusion, medical imaging, signal processing, pharmacovigilance, and business analytics. Author, Poisson Point Processes, Springer, 2010 (Chinese translation, Science Press, 2013). Co-author, Bayesian Multiple Target Tracking, 2nd Edi- tion, Artech, 2014. Seven US patents. He is the co-author of the book entitled Analytic Combinatorics for Multiple Object Tracking, Springer, scheduled to be published in December 2020.

Blair Angle is a senior research scientist at Metron, Inc. Since joining Metron in 2008, he has worked as the technical lead on a variety of projects involving mathematical and statistical modeling, machine learning, tracking, simulation, signal processing, and software development. During his tenure at Metron, he has written or co-written several proposals for DARPA, ONR, etc. which have led to new Metron funding and research. His current research involves multiple- object tracking, with a focus on applying analytic combinatorial (AC) methods to data association problems. Along with Dr. Roy Streit, he recently developed and implemented a working version of the Multisensor JiFi (JPDA intensity Fil- ter), a multisensor, multiobject tracking filter for extended objects. He is the co-author of thebook entitled Analytic Combinatorics for Multiple Object Tracking, Springer, scheduled to be published in December 2020.

Murat Efe is a full Professor and Head of the Electrical and Electronics Engi- neering Department at Ankara University. He has publisher numerous papers in refereed journals, conferences, and seminars on target tracking/data fusion. He is an Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and was one of the lecturers for the NATO-CSO Lecture Series entitled “Radar and SAR Systems for Airborne and Space-based Surveillance and Reconnaissance” between 2013-2017 where a total of 13 countries, namely Italy, UK, France, Spain, Germany, Romania, US, Canada, Portugal, Lithuania, Bulgaria, Poland and Australia were visited for these lectures. Dr. Efe is a technical consultant to a number of defense companies on tracking and fusion related projects. Also, he served on the executive board of the Electrical, Electronics and Informatics Research Group of the Scientific and Technological Research Council of Turkey. Dr. Efe has been an elected member of Board of Directors of ISIF since 2014 where his term ends in 2023. He is the co-author of the book entitled Analytic Combinatorics for Multiple Object Tracking, Springer, scheduled to be published in December 2020.

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13.8 Passive Radar – From Target Detection to Imaging [MP-1]

Instructors

• Dr. Mateusz Malanowski, Warsaw University of Technology • Dr. Piotr Samczyński, Warsaw University of Technology • Dr. Krzysztof Kulpa, Warsaw University of Technology

Abstract

The topic of the tutorial is multistatic passive radar for target detection and imaging. In the first part of the tutorial the basics of passive radar will be presented. These include a review of possible illuminators of opportunity (e.g. FM radio, digital television, cellular telephony), and features of different signals from the point of view or radar detection. The bi/multi-static geometry will be presented, and passive radar equation will be analyzed.

The second part will be focused on detection and tracking of airborne targets using passive radar. A typical signal processing chain, consisting of clutter filtering, crossambiguity function calculation, detection, bistatic tracking and Cartesian tracking, will be described. Selected results and applications will be shown. The third part will be devoted to target imaging using passive radar. This will be focused on ISAR (Inverse Synthetic Aperture Radar) mode, where images of targets are created.

The last part of the tutorial will outline possible future applications of passive radar. These include passive radar on moving platforms, e.g. airborne or seaborne. Terrain mapping in the SAR (Syn- thetic Aperture Radar) mode will be presented as one of the possible applications of passive radar on a moving platform. At the end, the concept of Deployable Multiband Passive/Active Radar will be presented, in which a combination of active and passive radars is used.

Instructor Biographies

Prof. Mateusz Malanowski received his M.Sc., Ph.D. and D.Sc. degrees in Elec- trical Engineering from the Warsaw University of Technology, Warsaw, Poland, in 2004, 2009 and 2013 respectively. He was a Research Scientist with FGAN (Forschungsgesellschaft fuer Angewandte Naturwissenschaften), Germany, and an Engineer with Orpal, Poland. Currently, he is an Associate Professor at the Warsaw University of Technology. Prof. Malanowski is the author/coau- thor of over 180 scientific papers. He is also an author of “Signal Processing for Passive Bistatic Radar” book, published by Artech House. His research interests are radar signal processing, target tracking, passive coherent location, synthetic aperture radar and noise radar. For the last 14 years he has been involved in numerous national and international projects, focusing on passive radar, synthetic aperture radar and noise radar. He has been a member of several NATO Science and Technology Organization groups. Prof. Malanowski is currently managing a project, whose aim is to develop first Polish, and one of the first in the world, operational military (TRL9) passive radar system. Prof. Malanowski isaIEEE Senior Member and a member of Institution of Engineering and Technology (IET) and European Microwave Association (EuMA).

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Prof. Piotr Samczynski received his B.Sc. and M.Sc. degrees in electronics and Ph.D. and D.Sc. degrees in telecommunications, all from the Warsaw Uni- versity of Technology (WUT), Warsaw, Poland in 2004, 2005, 2010 and 2013 respectively. Since 2018, he has been the Associate Professor at the WUT; and since 2014 – a member of the WUT’s Faculty of Electronics and Information Technology Council. Prior to this, he was Assistant Profesor at WUT (2018- 2010), a research assistant at the Przemyslowy Instytut Telekomunikacji S.A. (PIT S.A.) (2010-2005) and the head of PIT’s Radar Signal Processing Depart- ment (2010-2009). Prof. Samczynski’s research interests are in the areas of radar signal processing, passive radar, synthetic aperture radar and digital signal processing. He is the author of over 200 scientific papers. Prof. Samczynski was involved in several projects for the European Research Agency (EDA), Polish National Centre for Research and Development (NCBiR) and Polish Ministry of Science and Higher Education (MiNSW), including the projects on SAR, ISAR and passive radars. Since 2009 he has been a member of several research task groups under the NATO Science and Technology Organization, where he supports the research work in the fields of radar signal processing, modern passive and active radars architectures and noise radars. Since 2018 he is a Chair of NATO SET-258 research task group (RTG) on Deployable Multiband Pas- sive/Active Radar (DMPAR) deployment and assessment in military scenarios. Prof. Samczynski is an IEEE member since 2003, and IEEE Senior member since 2016. He is a member of IEEE AES, SP, and GRS Societies and since March 2017 Prof. Samczynski is a Chair of the Polish Chapter of the IEEE Signal Processing Society. He received IEEE Fred Nathanson Memorial Award for outstanding contribution to the field of passive radar imaging, including systems design, experimentation and algorithm development in 2017.

Prof. Krzysztof Kulpa received his M. Sc., Ph.D. and D.Sc. degrees from the Warsaw University of Technology (WUT) in 1982, 1987 and 2009 respectively. Since 1990 he is with Institute of Electronic Systems (WUT), working on Radar Technology, including SAR, ISAR, passive and noise radars. Since 2011 he is of Scientific Director of the Defense and Security Research Center at WUT.In 2014 he obtained the title of State Professor. He has had more than 250 published papers, and recently had his book “Signal Processing in Noise Waveform Radar” published by Artech House Publishers. In his professional life he has always combined teaching, theoretical research and applications. He has been involved in several application projects and worked for the Polish radar industry for 15 years.

13.9 Advanced Inverse Synthetic Aperture Radar Imaging [MP-2]

Instructor

• Dr. Marco Martorella, University of Pisa and Radar and Surveillance Systems Laboratory

Abstract

Inverse Synthetic Aperture Radar (ISAR) is a technique used for reconstructing radar images of moving targets. Often, modern high-resolution radars implicitly offer the system requirements

77 IEEE RADARCONF 2021 13 TUTORIALS needed for implementing ISAR imaging. ISAR images can be obtained by means of a signal processing that can be enabled both on and off-line by using dedicated image formation algorithms. Automatic Target Recognition (ATR) systems are often based on the use of radar images because they provide a 2D electromagnetic map of the target reflectivity. Therefore, classification features that contain spatial information can be extracted and used to increase the performance of classifiers. The understanding of ISAR image formation is crucial for optimising ATR systems thatare based on such images. This tutorial will start with an introduction of ISAR imaging and will then focus on a number of advanced ISAR techniques and applications, including but not limited to Pas- sive ISAR (P-ISAR), Polarimetric ISAR (Pol-ISAR), Compressed-Sensing-Based ISAR (CS-ISAR) and Three-dimensional ISAR (3D-ISAR).

Instructor Biography

Marco Martorella received his Laurea degree (Bachelor+Masters) in Telecom- munication Engineering in 1999 (cum laude) and his PhD in Remote Sensing in 2003, both at the University of Pisa. He is now an Associate Professor at the Department of Information Engineering of the University of Pisa and an external Professor at the University of Cape Town where he lectures within the Masters in Radar and Electronic Defence. Prof. Martorella is also Director of the CNIT’s National Radar and Surveillance Systems Laboratory. He is author of more than 200 international journal and conference papers, three books and 17 book chapters. He has presented several tutorials at international radar conferences, has lectured at NATO Lecture Series and organised international journal special issues on radar imaging topics. He is a Fellow of the IEEE, a member of the IET Radar Sonar and Naviga- tion Editorial Board and a member of AFCEA. He is also a member of the IEEE AES Radar Systems Panel, a member of the NATO SET Panel, where he sits as co-chair of the Radio Frequency Technol- ogy Focus Group, and a member of the EDA Radar Captech. He has chaired several NATO research activities, including three Research Task Groups, one Exploratory Team and two Specialist Meet- ings. He has been recipient of the 2008 Italy-Australia Award for young researchers, the 2010 Best Reviewer for the IEEE GRSL, the IEEE 2013 Fred Nathanson Memorial Radar Award, the 2016 Out- standing Information Research Foundation Book publication award for the book Radar Imaging for Maritime Observation and the 2017 NATO Set Panel Excellence Award. He is a co-founder of ECHOES, a radar systems-related spin-off company. His research interests are mainly in the field of radar, with specific focus on radar imaging, multichannel radar and space situational awareness.

13.10 Digital Array Radar [MP-3]

Instructors

• Dr. Caleb Fulton, University of Oklahoma • Kenneth W. O’Haver, Johns Hopkins Applied Physics Laboratory • Dr. Salvador H. Talisa, Johns Hopkins Applied Physics Laboratory • Dr. Mark Yeary, University of Oklahoma

78 IEEE RADARCONF 2021 13 TUTORIALS

Abstract

With continuing advances in RF Integrated Circuits, digital data transport, and digital signal processing technologies, digital arrays are beginning to enter the mainstream of radar system development. Digital arrays offer new capabilities for radar, including large numbers of independent simultaneous receive beams and unprecedented levels of flexibility and re-configurability.

This tutorial will be devoted to the emerging technology of digital phased arrays and their applications to advanced radar systems. The tutorial will include a history of digital phased array development and of work completed on recent testbeds and small-scale demonstrators. It will also help provide the audience with an understanding and awareness of

• Differences between analog and digital architectures for arrays • The need for, and methods by which to achieve, dynamic range scaling and calibration • Challenges encountered when performing calibration • The opportunities, applications and future trends for CSWAP reduction • Application of dual/arbitrary polarization processing • The role of digital arrays in weather radar and satellite communications • Expected benefits from modern and next generation digital-array systems including: – Enhanced dynamic range – Full MIMO capability – Arbitrary, and even ubiquitous, formation of multiple simultaneous receive beams – Array scalability and modularity – System longevity through standardization • Current development efforts towards larger-scale demonstrators and testbeds • The associated beamforming, adaptive algorithms, etc., enabled by these new systems

Instructor Biographies

Dr. Caleb Fulton (S’05-M’11-S’16) received his B.S. and Ph.D. in ECE from Pur- due University in West Lafayette, IN, in 2006 and 2011, respectively, and is now an Associate Professor in ECE at the University of Oklahoma’s Advanced Radar Research Center in Norman, OK. His work focuses on antenna design, digital phased array calibration and compensation for transceiver errors, calibration for high-quality polarimetric radar measurements, integration of low- complexity transceivers and high-power GaN devices, and advanced digital beamforming design considerations. He is currently involved in a number of digital phased array research and development efforts for a variety of applications. He received the Purdue University Eaton Alumni Award for Design Excellence In 2009 for his work on the Army Digital Array Radar (DAR) Project. He also received the Meritorious Paper Award for a summary of these efforts at the 2010 Government Microcircuit Applications and Crit- ical Technologies Conference. More recently, he received a 2015 DARPA Young Faculty Award for his ongoing digital phased array research. Dr. Fulton is a member of the IEEE Antennas and Propa- gation, Aerospace and Electronic Systems, and Microwave Theory and Techniques Societies, and serves on the Education Committee of the latter.

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Kenneth W. O’Haver is a member of the Principal Professional Staff and Chief Scientist of the Sensor and Communications Systems Branch at The Johns Hopkins University Applied Physics Laboratory. His recent work has focused on digital array technology development for advanced radar and other applications. He has extensive experience on the development of phased array technologies and systems for a variety of applications including radar, electronic warfare, and communications systems. Mr. O’Haver holds a B.S. in Electrical Engineering from Virginia Polytechnic Institute and State University and an M.S. in Electrical Engineering from the Johns Hopkins University. He has authored and co-authored more than 20 articles.

Salvador H. Talisa is a member of the Principal Professional Staff and Chief Scientist of the Radar and Electronic Warfare Systems Development Group at The Johns Hopkins University Applied Physics Laboratory. His recent work is focused on advanced radar systems and technology and digital arrays, including the radar performance impact of distributed receivers and exciters. He has conducted research on microwave magnetic, magneto-optic and microwave superconducting devices and materials at the Westinghouse (later Northrop Grumman) Science and Technology Center in Pittsburgh and was a program and business development manager for radar receiver and exciter technology at Northrop Grumman Electronic Systems. Dr. Talisa is a Life Senior Member of the IEEE and holds a Telecommunications Engineering degree from the Polytechnic University of Catalonia in Barcelona, Spain, and M.Sc. and Ph.D. degrees in (Electrical) Engineering from Brown University. He has authored and co-authored more than 60 articles and was awarded 7 patents.

Dr. Mark Yeary graduated with his Ph.D. in Electrical Engineering from Texas A&M University in 1999. He is a founding member of Advanced Radar Re- search Center (ARRC) at the University of Oklahoma (OU) in Norman, OK and was named a Hudson-Torchmark Presidential Professor in 2011. His research interests are in the areas of digital signal processing (DSP) as applied to customized DSP systems, and instrumentation for radar systems with an emphasis on hardware prototype development. He has authored or co-authored 250+ conference papers, conference abstracts and journal papers in these areas. From 2003 to 2016, he was heavily involved in the SPY-1A phased array project at the National Weather Radar Testbed (NWRT) in Norman, OK when this radar was operational. He was a performer on the DARPA ACT program with the Rockwell-Collins team, 2013-2017. He is currently a teammate on the ARRC’s S-band all-digital mobile phased array radar program known as Horus. Dr. Yeary was General Co-Chair of the 2018 IEEE Radar Conference held in Oklahoma City. He is a licensed Professional Engineer (PE) and IEEE Fellow.

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13.11 Deep Learning Applications for Radar Systems with MATLAB [MP-4]

Instructors

• Rob Graessle, MathWorks, Inc. • Rick Gentile, MathWorks, Inc. • Dr. Honglei Chen, MathWorks, Inc.

Abstract

Modulation identification and target classification are important functions for intelligent RFre- ceivers. These functions have numerous applications in cognitive radar, software-defined radio, and efficient spectrum management. Machine Learning and Deep Learning techniques canbe used in these applications to successfully classify radar data.

In this tutorial, we will demonstrate a range of different techniques to:

1. Collect data from off-the-shelf radars and software-defined radios to train and test classifiers 2. Label I/Q data collected from radar hardware 3. Synthesize data to train Deep Learning and Machine Learning networks for a range of radar and wireless communications systems 4. Explore radar signals in the spectral and time-frequency domains 5. Perform pre-processing and feature extraction for machine learning and deep learning applications 6. Input data and features into networks and configure network architectures for the best performance 7. Interface to deep learning networks outside MATLAB

We will use real world examples to demonstrate these techniques including:

1. Radar RCS identification 2. Radar/comms waveform modulation ID 3. Micro-Doppler signatures for target identification (for example, pedestrians, bicycles, aircraft with rotating blades) 4. RF Fingerprinting 5. Anomaly detection for tracking and sensor fusion applications 6. Synthetic Aperture Radar (SAR) Attendees will learn: 7. How to make data set trade-offs between machine learning and deep learning workflows 8. Implement efficient ways to work with 1D and 2D (time-frequency) signals 9. Extract features that can be used to improve classification results 10. Validate designs with over-the-air signals from software-defined radios (SDR) and radars.

A pdf version of the slides, all of the tutorial examples, along with a temporary license, will be provided for attendees to explore the concepts covered in this tutorial.

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Instructor Biographies

Rob Graessle is a Senior Applications Engineer at MathWorks focused on wireless communications, radar systems, and software-defined radio. He previously worked for the Air Force Research Laboratory, Sensors Directorate, and holds a B.S. and M.S. from Miami University.

Rick Gentile focuses on Phased Array, Signal Processing, and Sensor Fusion applications at MathWorks. Prior to joining MathWorks, Rick was a Radar Sys- tems Engineer at MITRE and MIT Lincoln Laboratory, where he worked on the development of many large radar systems. Rick also was a DSP Applications Engineer at Analog Devices where he led embedded processor and system level architecture definitions for high performance signal processing systems, including automotive driver assist systems. Rick co-authored the text “Embed- ded Media Processing”. He received a B.S. in Electrical and Computer Engineer- ing from the University of Massachusetts, Amherst and an M.S. in Electrical and Computer Engineering from Northeastern University, where his focus areas of study included Mi- crowave Engineering, Communications and Signal Processing.

Honglei Chen is a Principal Engineer at MathWorks, Inc. where he leads the development of phased array system simulation. He received his Bachelor of Science from Beijing Institute of Technology and his MS and PhD, both in Elec- trical Engineering, from the University of Massachusetts, Dartmouth.

13.12 Efficient Spectral Access for Radar and Communications [MP-5]

Instructors

• Dr. Cenk Sahin, Sensors Directorate, Air Force Research Laboratory • Dr. Patrick McCormick, Sensors Directorate, Air Force Research Laboratory • Dr. Justin Metcalf, University of Oklahoma

Abstract

The electromagnetic spectrum (EMS) is a precious resource that connects and protects our societies across the globe. However, the spectrum has become increasingly congested with no end in sight. To mitigate this congestion, it is vital that future users of the spectrum do so in an efficient

82 IEEE RADARCONF 2021 13 TUTORIALS manner. As radar and communication systems pose the greatest demand on spectrum access, their future designs must make use of all degrees-of-freedom (DoF): time, frequency, space, coding and polarization. Technologies for efficient radar-communications spectral access can be grouped into two broad categories: co-design and coexistence. Coexistence is where radar and communications systems must share a set band while a co-designed radar/communications system uses a single, flexible RF aperture to time-multiplex or emit dual-function waveforms.

Successful co-existence and co-design of radar and communication systems both rely on fundamental understanding of the design goals, constraints, and performance metrics of both types of systems, which are not related to each other in a mathematically tractable fashion. Therefore, this tutorial will provide a first-principles examination of the design goals and metrics of both radar and communications. We will explore the motivation and history of spectrum access and examine the practical requirements for utilizing the available DoFs. Specific examples of coexistence and co-design techniques will be explored based on the DoF(s) they use to enable efficient spectrum access. Implications of hardware constraints on these techniques will be illustrated. To narrow the focus radar detection will be the primary radar application.

We hope this tutorial will provide a strong foundation to introduce both experienced and novice practitioners of both radar and communications research into the area of efficient spectrum access. The congested spectrum is our new reality, and future RF engineers will have to understand and operate within this reality.

Instructor Biographies

Dr. Cenk Sahin received his B.S. degree in electrical and computer engineering from the University of Missouri – Kansas City, MO in 2008, and his M.S. and Ph.D. degrees both in electrical engineering, from the University of Kansas, Lawrence, KS, in 2012 and 2015, respectively. He received the Richard K. & Wilma S. Moore PhD Dissertation Award given to the best PhD dissertation in the Department of Electrical Engineering & Computer Science, University of Kansas. For his PhD work he characterized channel coding and latency performance (coding and queueing delay, and achievable data throughput) of various practical modulation schemes over wireless fading channels. In 2016 Cenk Sahin was awarded the prestigious National Research Council (NRC) Postdoctoral Research Fellowship. As an NRC fellow at AFRL (2016-2018) he developed a family of spectrally-efficient constant-modulus radar-embedded communications waveforms. Since 2018 he has been with the RF Technology Branch at the Sensors Directorate, Air Force Research Laboratory, WPAFB, OH where he has been leading the design, development, and testing of spectrum-sharing methods for radar and communications, radar-embedded communication waveforms, and signal processing techniques for dual-function systems. He is the author of 5 journal papers, 6 (1 issued, 5 pending) patents (5 in the area of dual-function system design), 22 peer-reviewed conference papers and 2 book chapters.

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Dr. Patrick M. McCormick received his BS in Mechanical Engineering (May 2008), his BS in Electrical Engineering (May 2013) and his Ph.D. in Electrical Engineering from the University of Kansas (May 2018). In August of 2018 he began his current position with the Air Force Research Laboratory (AFRL) Sensors Directorate as a Research Electronics Engineer at Wright-Patterson Air Force Base in Ohio researching dual-function radar and communications co-design. Dr. McCormick has over 40 peer-reviewed publications including 5 journal, 35 conference, and 4 book chapters as well as 4 patents (pending) in the area of waveform design and implementation. He has made significant contributions to radar waveform design, adaptive receive signal processing, and new forms of waveform diversity and radar/communication spectrum sharing. He has either led or supported 18 open-air and 9 benchtop experiments to evaluate new radar emission structures and/or new receive processing schemes. Dr. McCormick has received numerous awards and honors including the 2018 Robert T. Hill Best Dissertation Award; PhD EE with honors in May 2018; University of Kansas Electrical En- gineering and Computer Science Department Nominee for Outstanding Doctoral Researcher; 1st Place in the 2018 IEEE Radar Conference Student Paper Competition; and 3rd place in the 2016 IEEE Radar Conference Student Paper Competition.

Dr. Justin Metcalf received his BS in Computer Engineering from Kansas State University in 2006. From 2006-2008 he was at the Flight Simulation Labs of Lockheed Martin Aeronautics in Fort Worth, TX. From 2008-2014 he was with the Radar Systems Lab of the University of Kansas, were he obtained an MS in Electrical Engineering in 2011 and a PhD in Electrical Engineering in 2015. He was the recipient of the Richard and Wilma Moore Award for the best de- partmental MS thesis in 2011-2012. He was with the Sensors Directorate of the Air Force Research Laboratory from 2014-2018. He was the chair of the Dayton Chapter of the IEEE Aerospace and Electronic Systems Society from 2016-2018 and won the 2017 IEEE Dayton Section Young Professionals Award. Since 2018 he has been an Assistant Professor with the Electrical and Computer Engineering Department at the University of Oklahoma, and a member of the Advanced Radar Research Center. He was the recipient of a 2020 DARPA Young Faculty Award and is a member of the Radar Systems Panel. He has published 47 peer-reviewed publications, including 38 conference papers, 6 journal papers, and 3 book chapters, as well as a patent and 5 pending patent applications on topics related to radar signal processing, waveform diversity, radar-embedded communications, and game theory. He has been active in radar/communications research for more than 11 years.

13.13 Virtual RF Environments to Support Advanced Radar Mode Development [FA-1]

Instructors

• Dr. Joseph Guerci, Information Systems Laboratories, Inc. • Dr. Brian Watson, Information Systems Laboratories, Inc. • Dr. Sandeep Gogineni, Information Systems Laboratories, Inc. • Dr. Radu Visina, Information Systems Laboratories, Inc. • Dr. Hoan Nguyen, Information Systems Laboratories, Inc.

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Abstract

In an increasingly crowded spectral environment, radar and RF systems have evolved to adaptively and cooperatively exploit the limited spectrum. Cognitive systems that Sense, Learn, and Adapt (SLA) using conventional processing or by leveraging artificial intelligence are becoming more common. These systems continuously estimate the RF channel and adjust their emissions to extract the most information from the limited available spectrum. It is difficult to predict the behavior of these systems during design and laboratory testing as their behavior depends on the particular RF environment. It is nearly impossible to test RF systems in operational or contested environments whose behavior depends dynamically on the RF spectrum.

Part I of this tutorial is an overview of high-fidelity Modeling and Simulation (M&S) with a focus on particular applications including Cognitive Radar, Deep Learning, and Cognitive Radar Sched- ulers. M&S is a key enabler of intelligent RF systems. For instance, antagonist tactics (e.g., cell site simulation, jamming, spoofing) are constantly changing. Flight tests are extremely expensive and difficult to schedule and do not accurately replicate contested environments. In the future, engineers will increasingly rely on sophisticated M&S tools for RF system design.

In Part II, high-fidelity M&S techniques are described in detail including the traditional covariance- based modeling of radar clutter channels, Green’s function modeling of radar clutter channels, and real-time Hardware-in-the-Loop (HWIL) testing to emulate complex environments for radar and communication systems.

In Part III, four real-world examples of high-fidelity M&S techniques are presented entitled: Urban Tracking, Cognitive Radar, Deep Learning for RF Data, and Cognitive Radar Schedulers. These examples encompass M&S for generation of the expected radar returns for complex environments, the SLA paradigm including channel estimation and optimal waveform design, generation of an- notated I&Q training data for deep learning, and the optimal control and management of multiple sensors with multiple modes.

Instructor Biographies

J. R. Guerci has 30 years of experience in advanced technology research and development in government, industrial, and academic settings including the US Defense Advanced Research Projects Agency (DARPA) as Director of the Special Projects Office (SPO) where he led the inception, research, development, execution, and ultimately transition of next generation multidisciplinary defense technologies. In addition to authoring over 100 peer reviewed articles in next generation sensor systems, he is the author of Space-Time Adaptive Processing for Radar, 2nd Ed., and Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach, (Artech House). In 2020 he received the IEEE Dennis J. Pi- card Medal for contributions to advanced radar technology. He is currently President and CEO of Information Systems Laboratories, Inc.

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Brian Watson is the Chief Technology Officer for Information Systems Labora- tories with over 25 years of experience. He worked for the Navy and Air Force as an electronics engineer in support of DOD programs including the Tomahawk Weapons System and F-111 aircraft. The topic of his thesis was magnetic and acoustic measurements on low-dimensional magnetic materials. The primary purpose was to understand the quantum mechanical mechanism governing high temperature superconductivity. He designed and built a 9 Tesla nuclear magnetic resonance system that can operate at temperatures near 1 Kelvin. He was the winner of the University of Florida Tom Scott Memorial Award for distinction in experimental physics. Dr. Watson has experience with Labview programming, analog RF electronics design, PCB design and layout, high-speed data acquisition systems, matching networks, and low-noise electronics. Dr. Watson was the PI for ONR and DARPA programs that investigated neural network architectures in collaboration with the Salk Institute and the University of California at San Diego. In that role, he designed asynchronous recurrent neural networks for high-speed parallel data processing. More recently, he has developed deep learning neural networks for SIGINT and ELINT applications. He is the co-author of Non-Line-of-Sight Radar (Artech House).

Sandeep Gogineni is a Research Scientist for Information Systems Laborato- ries with over 12 years of experience working on radar and wireless communications systems. He has worked for 6 years as an on-site contractor for Air Force Research Laboratory (AFRL), developing novel signal processing algorithms and performance analysis for passive radar systems. He received the IEEE Dayton Section Aerospace and Electronics Systems Society Award for these contributions to passive radar signal processing. Prior to his time at AFRL, during his graduate studies at Washington University in St. Louis, Dr. Gogineni developed optimal waveform design techniques for adaptive MIMO radar systems and demonstrated improved target detection and estimation performance. At ISL, Dr. Gogineni has been working on channel estimation algorithms and optimal probing strategies for MIMO radar systems in the context of Cognitive Fully Adaptive Radar (CoFAR). Additionally, Dr. Gogineni and his colleagues at ISL have demonstrated the feasibility of using neural networks and artificial intelligence techniques to solve extremely challenging radio frequency (RF) sensing problems. His expertise includes statistical signal processing, detection and estimation theory, deep learning, artificial intelligence, performance analysis, and optimization techniques with applications to active and passive RF sensing systems.

Hoan K Nguyen is a Principal Research Scientist at ISL and a principle investigator (PI) and co-PI on several AFRL and NAVAIR-sponsored cognitive radar and cognitive fully adaptive radar (CoFAR) scheduler projects. She has over 15 years of experience providing direct analytic support to multiple three-star flag officers in the U.S. military. Hoan served as a special staff to theComman- der, Naval Air Forces, Commanding General, III MEF, Commander, U.S. Third Fleet, U.S. Army Rapid Equipping Force, and the Combined Joint Task Force Paladin. In her roles, Hoan deployed with the staff to Afghanistan, Nepal, and the Philippines, for real world operations and embarked on multiple Naval platforms, such as aircraft carriers, amphibious ships, and MV-22, just to name a few. She also led a wide range of studies to include the development of new training readiness metrics, operational assessment metrics, mishaps analysis, combat power analysis, OPLAN feasibility analysis, and

86 IEEE RADARCONF 2021 13 TUTORIALS emerging technologies assessments. Dr. Nguyen has published over a dozen peer-reviewed papers and several dozen CNA research papers. Her current research interests include optimization methods, radar scheduling algorithms, and signal processing.

Radu Visina received the PhD in Electrical Engineering from the University of Connecticut in 2019 and has over 15 years of experience in the electrical, systems, and software engineering disciplines. Before pursuing graduate studies, Dr. Visina designed and developed high-performance, high-precision control systems and supporting software for the sensor calibration as well as power systems industries. Passion and curiosity in these fields moved him to academia, where he studied and contributed to systems engineering with a focus on radar target tracking. Dr. Visina has since made significant contributions in the field and maintains expertise in the challenging sub-fields of nonlinear estimation & feedback control, maneuvering/multiple model target tracking, nonlinear information fusion (including track-to-track fusion), Bayesian decision theory, and urban target tracking using non-line-of-sight radar measurement extractions. Dr. Visina’s current position at ISL is Technical Lead Research Engineer, developing real-time RF simulation tools and knowledge- aided target tracking techniques.

13.14 Deep Learning for Radio Frequency Automatic Target Recognition (ATR) [FA-2]

Instructors

• Dr. Uttam K. Majumder, Air Force Research Lab • Dr. Erik P. Blasch, Air Force Office of Scientific Research • Prof. David A. Garren, Naval Postgraduate School

Abstract

The focus of this course will be recent research results, technical challenges, and directions of deep learning (DL) based object classification using radio frequency data (i.e., Synthetic Aperture Radar (SAR) data, RF communication signals data). First, we will present ATR theory from the perspective of robust and reliable performance evaluation. We also discuss RF ATR research in the past (i.e., template-based approach) conducted under DARPA MSTAR (Moving and Stationary Target Acqui- sition and Recognition) program. Second, we will provide an overview of various machine learning (ML) theories applied to RF data and present important deep learning architectures/models. Third, we will demonstrate implementations and performance analysis (confusion matrix analysis, t-SNE plot, etc.) of DL-based ATR on SAR data. Finally, we will highlight advanced/specialized machine learning techniques such as transfer learning (TL), few-shot learning (FSL), adversarial machine learning (AML), and out of distribution (OOD) /open set problems applied to solving challenging ATR problems. Software/tools to be used include Python/PyTorch, Google colaboratory GPUs, Amazon’s Web Service (AWS) cloud computing, and/or TensorFlow. Datasets to be used include AFRL public released MSTAR, CVDome, and SAMPLE datasets, North Eastern University RF signals data, and ship detection datasets. Overall contents of this course will be our recently published (July 2020, Artech House) textbook “Deep Learning for Radar and Communications ATR”. We have presentation slides ready for the attendees.

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Instructor Biographies

Uttam K. Majumder, Ph.D., is a senior electronics engineer with the Air Force Re- search Laboratory (AFRL). He earned Ph.D. in electrical engineering from Pur- due University, M.S. in electrical engineering from Air Force Institute of Tech- nology, an MBA from Wright State University, and a B.S. in computer science from the City College of New York, CUNY. His research interests include artificial intelligence / machine learning (AI/ML), synthetic aperture radar (SAR) algorithms development for surveillance applications, radar waveforms design, and high performance computing for SAR based automatic target recognition (ATR). Dr. Majumder is a co-author of the book ”Deep Learning for Radar and Communications Automatic Target Recognition”. He is an IEEE AESS Distinguish Lecturer for the research area, “Deep Learning for RF Target Classification”. He delivered SAR Signal and Im- age Processing and RF ATR tutorials at several IEEE and SPIE conferences, symposiums, invited venues, and academic institutions. Dr. Majumder is a senior member of IEEE.

Erik P. Blasch, Ph.D., is a program officer with the Air Force Research Labo- ratory. He received his B.S. in Mech. Eng. from the Massachusetts Institute of Technology and Ph.D. in Electrical Eng. from Wright State University in addition to numerous Master’s Degrees in Mech. Eng., Ind. Eng., Elect. Eng., Medicine, Military Studies, Economics, and Business. Additionally, his assign- ments include Colonel (ret) in the USAF reserves, adjunct associate professor, and president of professional societies. His areas of research include information-fusion performance evaluation, image fusion, and human-machine integration; compiling over 900 papers, 30 patents, and 7 books. He is an As- sociate Fellow of AIAA, Fellow of SPIE, and Fellow of IEEE.

David A. Garren, Ph.D., has been a Professor in the Electrical and Computer Engineering Department at the Naval Postgraduate School (NPS) since 2012. He received the B.S. degree from Roanoke College in 1986, the Ph.D. degree from William & Mary in 1991, and was awarded an Office of Naval Research (ONR) Postdoctoral Fellowship at the Naval Research Laboratory (NRL) from 1991 through 1993. From 1994 through 2012, he held engineering positions at two Fortune-500 defense companies, which culminated in being awarded the titles of both Technical Fellow and Assistant Vice President. Professor Garren is a co-author of the book Deep Learning for Radar and Communications Auto- matic Target Recognition and has authored or co-authored 20 refereed journal papers and over 50 conference publications. He holds 7 U.S. Patents and is a Senior Member of the IEEE. In addition, he is an Associate Editor for the IEEE Transactions on Aerospace and Electronic Engineering.

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13.15 Multi-function Radar Resources Management [FA-3]

Instructor

• Dr. Alexander Charlish, Fraunhofer FKIE

Abstract

Electronically steered array technology combined with the capability to generate diverse waveforms significantly increases the ability of a radar to execute multiple functions. Such multifunction radar systems are capable of executing numerous tasks by multiplexing in time and angle. However, automated management techniques are required as fully controlling the available degrees of freedom is beyond the capability of a human operator. These management techniques are emerging as key performance factors for the next generation of multifunction radar systems. This tutorial introduces the topic of radar resources management. The core components of a radar manager will be described, and it will be shown how the components interact with each other and the radar signal processing. It will be shown how a radar can prioritize specific tasks, optimize the execution of radar tasks and divide constrained resources between tasks. Simulation examples will be used to demonstrate key concepts, such as adaptive tracking. The tutorial will also provide a perspective on how aspects such as cognitive radar, multifunction RF systems, multi-sensor management and machine learning impact on radar resources management.

The tutorial will follow the following schedule:

1. Resources management motivation 2. Review of background knowledge 3. Radar resource management architectures 4. Radar functions 5. Measurement scheduling 6. Task management 7. Priority assignment 8. Related topics

Instructor Biography

Alexander Charlish obtained his M.Eng. degree from the University of Notting- ham and received his Ph.D. degree from University College London on the topic of multifunction radar resources management. In 2011, he joined the Sensor Data and Information Fusion (SDF) Department at the Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE), where he now leads the Sensor and Resources Management Group. In this role, he leads a group of scientists conducting research on intelligent sensing with a focus on cognitive radar and resources management for sensor systems. Additionally, he is a visiting lecturer at RWTH Aachen University, where he teaches estimation and detection theory, as well as estimation, information fusion and machine learning. He is currently serving as an Associate Editor for Radar Systems for IEEE Transactions on Aerospace

89 IEEE RADARCONF 2021 13 TUTORIALS and Electronic Systems, and as Subject Editor for Radar, Sonar and Navigation for IET Electronic Letters. He is an elected member of IEEE AESS Radar Systems Panel and is currently serving as vice-chair of the panel. He is also active in the NATO community, where he currently co-chairs the Cognitive Radar Research Task Group. In 2019, he received the NATO SET Panel Excellence Award, and in 2020, he received the NATO SET Panel Early Career Award.

13.16 Micro-Doppler Signatures: Principles, Analysis and Applications [FA-4]

Instructors

• Dr. Carmine Clemente, University of Strathclyde • Dr. Francesco Fioranelli, Delft University of Technology

Abstract

The micro-Doppler analysis is the study of the time varying Doppler effect from multiple scattering centers with different dynamics. Over the past few years the potentials of micro-Doppler signature analysis has been demonstrated in areas such as enhanced target detection, characterization and tracking. The advantage of micro-Doppler resides in the distinctive Doppler modulations from different targets components that allow unique features to be obtained. These can be the basis of the development of algorithms for automatic target classification, that can increasingly leverage on advances in the field of deep learning. This topic is highly relevant to the conference asmicro- Doppler can play a significative role in modern radar systems in both civilian and defense applications. For instance, thanks to the enhancement in computational capabilities, the exploitation of micro-Doppler analysis is possible in a plethora of applications such as condition monitoring, urban surveillance, healthcare, automotive and manufacturing.

Contents Outline:

• Introduction: the introduction to the basic Doppler principle, definition of the micro-Doppler phenomenon, sampling, demodulation and data representation. • Time-Frequency Analysis: Wide-band and Narrowband Spectrogram, Gabor Uncertainty principle and Energy distributions. • Canonical Cases- Rigid Bodies: Basic principles of kinematic motion of rigid bodies. Micro- Doppler from vibrating, rotating and helicopters. • Non-Rigid Bodies: Modelling and simulation approaches, human gait and trotting of a Ger- man shepherd. • Signature Extraction Techniques: Extraction of the micro-Doppler from clutter and from the main body return. • Algorithms for Feature Extraction for Micro-Doppler Based ATR: Target recognition based on micro-Doppler. Principles of feature extraction and example of features extraction techniques applied to micro-Doppler. • Advanced emerging applications and techniques: Micro-Doppler for UAV classification, m- D Based ballistic threats discrimination, micro-Doppler in Industry 4.0 and Aggrotech, hand gesture recognition and vital sign monitoring, deep learning approaches for micro-Doppler applications.

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Instructor Biographies

Dr. Carmine Clemente is Senior Lecturer and Chancellor’s Fellow in Sensors Systems and Asset Management at the Department of Electronic and Electri- cal Engineering at the University of Strathclyde, Glasgow, UK since 2016. He obtained his PhD in Signal Processing from the University of Strathclyde in 2012. He received the Laurea cum laude (BSc) and Laurea Specialistica cum laude (MSc) degrees in Telecommunications Engineering from Universita’ degli Studi del Sannio, Benevento, Italy, in 2006 and 2009, respectively. Dr Clemente research interests lie on advanced radar signal processing algorithms, MIMO radars, passive radar systems and micro-Doppler analysis, extraction and classification. He published over 100 papers in journals and proceedings and he was co-recipient ofthe best student paper competition at the IEEE Radar conference 2015, and best paper at the Sensor Signal Processing for Defence Conference 2017.

Dr. Francesco Fioranelli is an Assistant Professor at TU Delft, the Netherlands, in the MS3 (Microwave Sensing Signals and Systems) research group. Be- tween April 2016 and October 2019, he was a Lecturer at the School of En- gineering, University of Glasgow. He received his Laurea cum laude (BEng) and Laurea Specialistica cum laude (MEng) degrees in telecommunication engineering from the Università Politecnica delle Marche, Ancona, Italy, in 2007 and 2010, respectively. He then obtained his PhD in through-wall radar imaging from Durham University, UK, in February 2014, and was a Research Associate on multistatic radar at University College London with Prof Hugh Griffiths from 2014 to March 2016. Dr Fioranelli has extensive expertise on the development and characterization of multistatic radar systems, and micro-Doppler radar signatures analysis for different applications. He has authored over 85 publications between book chapters, journal, and conferences, edited the recent book on “Micro-Doppler Radar and Its Applications” published by IET-Scitech, and received three best paper awards

13.17 Terahertz and Sub-Terahertz Automotive Radar: Emerging Technologies and Challenges [FA-5]

Instructors

1. Dr. Kumar Vijay Mishra, United States Army Research Laboratory 2. Dr. Bhavani Shankar M. R., University of Luxembourg 3. Dr. Marina Gashinova, University of Birmingham 4. Dr. Fatemeh Norouzian, University of Birmingham

Abstract

We are witnessing an autonomy race to get the first fully self-driving car on the road. The radar community has emerged at the forefront of fulfilling the promise of a self-driving car. As the automotive community inches closer to accomplishing this goal, more and newer problems get identified. This cycle is leading to the emergence of new technologies in automotive radar. Asaresult,

91 IEEE RADARCONF 2021 13 TUTORIALS current sophisticated automotive radars have much less in common with the radars developed just a few years ago.

In this context, there is a gradual push to sense the automotive environment not only at the millimeter- wave (mmWave) or sub-Terahertz band but also Terahertz (THz) frequencies. The high bandwidths available when using low-THz frequencies make it possible to distinguish between more closely spaced features in the reflected signal. At the same time, waves in this band are not suscepti- ble to complete obscuration by road dirt or precipitation, as infrared and optical systems would be. The mm-Wave and low THz bands also offer opportunities to combine sensing and communications. The joint-sensing-communications approaches do not easily extend to multiple-input multiple-output (MIMO) configurations and novel methods have been introduced in this context.

There has also been a quantum leap in automotive imaging techniques including synthetic aperture radar (SAR) processing for identifying objects from a moving vehicle. Many signal processing algorithms e.g. STAP earlier applied to only conventional radars are now increasingly re-interpreted and adapted for automotive radars. The displaced sensors and sensor-fusion are witnessing new developments in the context of automotive radar imaging. This tutorial will highlight the challenges and opportunities in these new technologies covering the mm-Wave and THz sensing and phenomenology, imaging systems, automotive SAR, interference mitigation, joint radar- communications, and the role of MIMO array processing in automotive radars.

• Part I: Introduction to Automotive THz and Sub-THz Sensing (55 mins)

– Characteristics of THz and mm-Wave channels – THz electronics technology for communications and sensing – Radar signal attenuation at low-THz for automotive scenarios – Radar cross-section of pedestrians at low-THz

• Part II: THz Automotive Radar (55 mins)

– Elements of THz radar design and capabilities – THz imaging in the context of automotive radar – Algorithms for image formation and processing (segmentation and classification) – Beamforming for THz imagery (SAR, DBS, phased array)

• Part III: Automotive MIMO Radar and MIMO Communications (55 mins)

– Fundamentals of mm-Wave MIMO radar and MIMO communications – MRMC scenarios and architectures – Interference in automotive radar – Automotive displaced MIMO sensor imaging – STAP processing for automotive applications

• Part IV: Automotive MRMC Co-existence and Co-design (55 mins)

– Monostatic radar with new MRMC waveforms – Bi-static MRMC with new and existing waveforms (PMCW, OFDMA, and 802.11ad) – MI-based design of automotive MRMC – Communications and radar sensor-fusion – Hardware prototype design and evaluation

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Instructor Biographies

Dr. Kumar Vijay Mishra obtained Ph.D. in electrical engineering and M.S. in mathematics from The University of Iowa in 2015, and M.S. in electrical engineering from Colorado State University in 2012, while working on NASA’s Global Precipitation Mission Ground Validation (GPM-GV) weather radars. He received his B. Tech. summa cum laude (Gold Medal, Honors) in electronics and communication engineering from the National Institute of Technology, Hamirpur (NITH), India in 2003. He is currently National Academies of Sci- ences, Engineering and Medicine (NASEM) Harry Diamond Distinguished Fel- low at United States Army Research Laboratory (ARL), Adelphi; technical adviser to Singapore-based automotive radar start-up Hertzwell; and honorary Research Fellow at SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg. He is the recipient of the Royal Meteorological Society Quarterly Journal Editor’s Prize (2017), Viterbi Postdoctoral Fellowship (2015, 2016), Lady Davis Postdoctoral Fellowship (2017), Technion EE Excellent Undergraduate Adviser Award (2017), DRDO LRDE Scientist of the Year Award (2006), NITH Director’s Gold Medal (2003), and NITH Best Student Award (2003). He has been Associate Editor (Radar Systems) of IEEE Transactions on Aerospace and Electronic Systems since 2020. His research interests include signal processing, remote sensing, electromagnetics, communications, and deep learning.

Dr. M. R. Bhavani Shankar received Masters and Ph. D in Electrical Communi- cation Engineering from the Indian Institute of Science, Bangalore in 2000 and 2007 respectively. He was a Post Doc at the ACCESS Linnaeus Centre, Sig- nal Processing Lab, Royal Institute of Technology (KTH), Sweden from 2007 to September 2009. He joined SnT in October 2009 as a Research Associate and is currently a Research Scientist at SnT. He was with Beceem Communi- cations, Bangalore from 2006 to 2007 as a Staff Design Engineer working on Physical Layer algorithms for WiMAX compliant chipsets. He was a visiting student at the Communication Theory Group, ETH Zurich, headed by Prof. Helmut Bölcskei during 2004. Prior to joining Ph. D, he worked on Audio Coding algorithms in Sasken Com- munications, Bangalore as a Design Engineer from 2000 to 2001. His research interests include Design and Optimization of MIMO Communication Systems, Radar and Array Processing, polynomial signal processing, Satellite communication systems, Resource Allocation, Game Theory, and Fast Algorithms for Structured Matrices. He is currently on the Executive Committee of the IEEE Benelux joint chapter on communications and vehicular technology, member of the EURASIP Spe- cial Area Team (SAT) on Theoretical and Methodological Trends in Signal Processing and serves as handling editor for Elsevier Signal Processing. He was a co-recipient of the 2014 Distinguished Contributions to Satellite Communications Award, from the Satellite and Space Communications Technical Committee of the IEEE Communications Society. He has co-organized special sessions in ICASSP (2017, 18), SPAWC (2015, 16) and EUSIPCO (2015, 16).

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Prof. Marina Gashinova received an M. Math degree from Saint-Petersburg State University, Saint Petersburg, Russia, in 1991, and a Ph.D. degree in physics and mathematics from Saint-Petersburg Electrotechnical University, Saint Pe- tersburg, Russia, in 2003. In 2006, she joined the Microwave Integrated System Laboratory, University of Birmingham, Birmingham, U.K., as a Research Fellow. She is currently a Professor of Radar and RF Sensors and leads a number of collaborative projects with academic and industrial partners, coordinating research on active and passive radar systems with a particular focus on sensing and imaging for situational awareness of autonomous platforms and sub-THz radar.

Dr. Fatemeh Norouzian received B.Eng from Amirkabir University of Technol- ogy, Iran, with First-Class Honours and M.Eng in Electronic and Communication Engineering from the University of Birmingham, UK, in 2008 and 2010, respectively. She was awarded a PhD degree at University of Birmingham in January 2015 for developing a methodology to design high-efficiency power amplifiers for Cognitive Radio Communication systems. Since then she is with the Mi- crowave Integrated Systems Laboratory as a research scientist, working within two main streams of research – short-range automotive sensing and THz sensing. Her principal research interests are in microwave and radar technologies, signal processing, fundamental EM theory, with a current focus on THz radar sensing, automotive sensing, modelling and characterization of signal propagation through diverse media, automotive radar signal processing for simulation of co-existence scenarios of autonomous platforms.

13.18 Bistatic and Multistatic Radar Imaging [FA-6]

Instructors

• Dr. Marco Martorella, University of Pisa • Dr. Brian Rigling, Wright State University

Abstract

SAR/ISAR images have been largely used for earth observation, surveillance, classification and recognition of targets of interest. The effectiveness of such systems may be limited by a number of factors, such as poor resolution, shadowing effects, interference, etc. Moreover, both SAR and ISAR images are to be considered as two-dimensional maps of the real three-dimensional object. Therefore, a single sensor may produce only a two-dimensional image where its image projection plane (IPP) is defined by the system-target geometry. Such a mapping typically creates a problem for the image interpretation, as the target image is only a projection of it onto a plane. In addition to this, monostatic SAR/ISAR imaging systems are typically quite vulnerable to intentional jammers as the sensor can be easily detected and located by an electronic counter-measure (ECM) system. Bistatic SAR/ISAR systems can overcome such a problem as the receiver can act covertly due to the fact that it is not easily detectable by an ECM system, whereas multistatic SAR/ISAR may push forward the system limits both in terms of resolution and image interpretation and add to the system resilience.

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Interest in bistatic and multistatic radar systems has been steadily increasing in the recent years, including those that also provide imaging capabilities. A NATO task group, namely the SET-250, on “Multidimensional radar imaging” is currently active that aims at demonstrating increased imaging capabilities of multi-static and multi-channel radar imaging systems. Also, the increased number of multi-bistatic passive radar imaging systems that are under study and development produce extra ground for multistatic radar imaging. The tutorial proposed addresses basics of bistatic and multistatic radar imaging and introduces the current state of the art and research activities in this field. The IEEE Radar conference is the optimal venue for proposing this timely subject.

1. Introduction to bistatic and multistatic radar

(a) Brief history (b) Bistatic and multistatic radar geometry Figures of merit and iso-contours

2. Bistatic Synthetic Aperture Radar

(a) Bistatic SAR geometry (b) Bistatic SAR signal modelling (c) Bistatic SAR imaging (d) Bistatic scattering phenomenology

3. Multistatic Synthetic Aperture Radar

(a) Multistatic SAR geometries (b) Multistatic SAR imaging (c) Three-dimensional imaging (d) Challenges to multistatic visualization

4. Bistatic Inverse Synthetic Aperture Radar (B-ISAR)

(a) B-ISAR geometry (b) B-ISAR signal modelling (c) Bistatically Equivalent Monostatic Theorem (d) B-ISAR image formation (e) B-ISAR applications: Emulated B-ISAR, Passive ISAR

5. Multistatic ISAR (M-ISAR)

(a) M-ISAR geometry (b) Coherent vs Incoherent Multistatic ISAR (c) Coherent M-ISAR: 3D-InISAR and STAP-ISAR (d) Incoherent M-ISAR: Multi-perspective ISAR imaging and Image fusion

95 IEEE RADARCONF 2021 13 TUTORIALS

Instructor Biographies

Brian Rigling received the B.S. degree in physics-computer science from the University of Dayton in 1998 and received the M.S. and Ph.D. degrees in electrical engineering from The Ohio State University in 2000 and 2003, respectively. From 2000 to 2004 he was a radar systems engineer for Northrop Grumman Electronic Systems in Baltimore, Maryland. Since July 2004, Dr. Rigling has been with the Department of Electrical Engineering, Wright State University, and was promoted to associate professor in 2009, professor in 2013, department chair in 2014, and Dean of Engineering & Computer Science in 2018. For 2010, he was employed at Science Applications International Corporation as a Chief Scientist while on leave from Wright State University. He has authored chapters for 4 textbooks and has authored more than 110 conference and journal papers. In 2007, Dr. Rigling authored the chapter on Bistatic Synthetic Aperture Radar for the book Advances in Bistatic Radar, edited by Nicholas Willis and Hugh Griffiths. Dr. Rigling served on the IEEE Radar Systems Panel 2009-2018, and has been an associate editor for IEEE Transactions on Image Processing. He was the General Chair for the 2014 IEEE Radar Conference, was awarded the 2015 IEEE Fred Nathanson Memorial Radar Award, and was elevated to IEEE Fellow in 2018.

96 IEEE RADARCONF 2021 14 RADAR SUMMER SCHOOL

14 Radar Summer School

The summer school is a brief overview of a wide range of radar topics which can be highly valuable for those starting a career in radar. Please consult the website for more information on access and timing.

14.1 Daily Schedule

Saturday, May 8

Time (UTC-4) Title Instructor

8:00 - 9:10 Radar Systems Alfonso Farina

9:10 - 10:20 Intro to Radar Signal Processing Hugh Griffiths

10:20-10:35 Break

10:35 - 11:45 Estimation and Detection Maria Greco

11:45 - 1:00 Break

1:00 - 2:10 Clutter Simon Watts

2:10 - 3:20 STAP/GMTI Mike Picciolo and Scott Goldstein

3:20 - 3:35 Break

3:35 - 4:45 Radar Waveforms Shannon Blunt

Sunday, May 9

Time (UTC-4) Title Instructor

8:00 - 9:10 Radar Imaging Marco Martorella

9:10 - 10:20 ATR Willie Nel

10:20 - 10:35 Break

10:35 - 11:45 Tracking Alex Charlish

11:45 - 1:00 Break

1:00 - 2:10 Passive/Distributed/MIMO Radar Braham Himed

2:10 - 3:20 Weather Radar Bob Palmer

97 IEEE RADARCONF 2021 14 RADAR SUMMER SCHOOL

14.2 Instructors

Dr. Shannon D. Blunt is the Roy A. Roberts Distinguished Professor of Electri- cal Engineering & Computer Science (EECS) at the University of Kansas (KU), Director of the KU Radar Systems Lab (RSL), and Director of the Kansas Applied Research Lab (KARL). He received a PhD in electrical engineering from the Uni- versity of Missouri in 2002, and from 2002 until he joined KU in 2005 he was with the Radar Division of the U.S. Naval Research Laboratory (NRL) in Wash- ington, D.C. His research interests are in sensor signal processing and system design with a particular emphasis on waveform diversity and spectrum sharing techniques, having made a variety of contributions that have been deployed in operational radar and sonar systems.

Prof. Blunt received an AFOSR Young Investigator Award in 2008, the IEEE/AESS Nathanson Memo- rial Radar Award in 2012, was named a Fellow of the IEEE for “contributions to radar waveform diversity and design” in 2016, was appointed to the U.S. President’s Council of Advisors on Science & Technology (PCAST) in 2019, and received the 2020 IET Radar, Sonar & Navigation Premium Award. He has likewise received multiple teaching awards. He has over 180 refereed journal, conference, and book chapter publications, and 16 patents/patents-pending. He co-edited the books Principles of Waveform Diversity & Design (2010) and Radar & Communication Spectrum Sharing (2018).

Alexander Charlish obtained his M.Eng. degree from the University of Notting- ham in 2006 and received his Ph.D. degree from University College London in 2011 on the topic of multifunction radar resources management. In 2011, he joined the Sensor Data and Information Fusion (SDF) Department at the Fraun- hofer Institute for Communication, Information Processing and Ergonomics (FKIE), where he now leads the Sensor and Resources Management Group. In this role, he leads a group of scientists conducting research on intelligent sensing with a focus on cognitive radar and resources management for sensor systems. Additionally, he is a visiting lecturer at RWTH Aachen University. He is currently an Associate Editor for Radar Systems for IEEE Transactions on Aerospace and Elec- tronic Systems, and a Subject Editor for Radar, Sonar and Navigation for IET Electronic Letters. He is a senior member of the IEEE, a member of the IEEE AESS Board of Governors for the term 2021

98 IEEE RADARCONF 2021 14 RADAR SUMMER SCHOOL

– 2023, and is currently vice-chair of the IEEE AESS Radar Systems Panel. He is also active in the NATO community, where he currently co-chairs the Cognitive Radar Research Task Group. He has received the NATO SET Panel Excellence Award and the 2019 NATO SET Panel Early Career Award.

Hugh Griffiths holds the THALES/Royal Academy Chair of RF Sensors in the De- partment of Electronic and Electrical Engineering at University College London, England. From 2006–2008 he served as Principal of the Defence Academy Col- lege of Management and Technology. He received the MA degree in Physics from Oxford University in 1975, then spent three years working in industry, before joining University College London, where he received the PhD degree in 1986 and the DSc(Eng) degree in 2000, and served as Head of Department from 2001–2006.

His research interests include radar systems and signal processing (particularly bistatic radar and synthetic aperture radar), and antenna measurement techniques. He serves as Editor-in-Chief of the IET Radar, Sonar and Navigation journal. He has published over five hun- dred papers and technical articles in the fields of radar, antennas and sonar. He has received several awards and prizes, including the IEEE Picard Medal (2017), IET Achievement Medal (2017), the IEEE AES Mimno Award (2015), the IET A.F. Harvey Prize (2012) and the IEEE AES Nathanson Award (1996). He is a Fellow of the IET (previously IEE), Fellow of the IEEE, and in 1997 he was elected to Fellowship of the Royal Academy of Engineering.

99 IEEE RADARCONF 2021 14 RADAR SUMMER SCHOOL

Dr. Scott Goldstein serves as Vice President of Engineering, Integration and Logistics Division at SAIC. Previously he was the Chief Strategy and Technol- ogy Officer of ENSCO and Chief Technologist for Dynetics, Inc., and theMan- ager of the Advanced Missions Solutions Group in Chantilly, VA. He has over 30 years of operational, engineering, leadership and management experience. He has performed fundamental research and development in Radar detection and estimation theory, Space Time Adaptive Processing, as well as in advanced systems concepts involving intelligence sensors, ISR, space superiority capabilities and cyber exploitation. He is a Fellow of the IEEE (for contributions to adaptive detection in radar and communications), a Fellow of the Washington Academy of Sci- ences and a member of the IEEE Radar Systems Panel. He received the 2002 IEEE Fred Nathanson Radar Engineer of the Year Award.

In 1993 she joined the Dept. of Information Engineering of the University of Pisa, where she is Full Professor since 2017. She’s IEEE fellow since Jan. 2011. She was co-recipient of the 2001 and 2012 IEEE Aerospace and Elec- tronic Systems Society’s Barry Carlton Awards for Best Paper, co-recipient of 2019 EURASIP JASP Best Paper Award, and recipient of the 2008 Fred Nathanson Young Engineer of the Year award for contributions to signal processing, estimation, and detection theory and of IEEE AESS Board of Governors Exceptional Service Award for “Exemplary Service and Dedication and Professionalism, as EiC of the IEEE AES Magazine”. In May-June 2015 and in January-February 2018 she visited as invited Professor the Université Paris-Sud, CentraleSupélec, Paris, France.

She has been general-chair, technical program chair and organizing committee member of many international conferences over the last 10 years. She has been lead-guest editor for the special issue on “Advances in Radar Systems for Modern Civilian and Commercial Applications”, IEEE Sig- nal Processing Magazine, July/September 2019, guest editor of the special issue on “Machine Learning for Cognition in Radio Communications and Radar” of the IEEE Journal on Special Topics of Signal Processing, lead guest editor of the special issue on “Advanced Signal Processing for Radar Applications” of the IEEE Journal on Special Topics of Signal Processing, guest co-editor of the special issue of the Journal of the IEEE Signal Processing Society on Special Topics in Signal Processing on “Adaptive Waveform Design for Agile Sensing and Communication”, and lead guest editor of the special issue of International Journal of Navigation and Observation on” Modelling and Processing of Radar Signals for Earth Observation. She is Associate Editor of IET Proceed- ings – Sonar, Radar and Navigation, and IET-Signal Processing, and Editor in Chief of the Springer Journal of Advances in Signal Processing (JASP). She is member of the IEEE AESS Board of Gov- ernors and has been member of the IEEE SPS BoG (2015-17) and Chair of the IEEE AESS Radar Panel (2015-16). She has been as well SPS Distinguished Lecturer for the years 2014-2015, AESS Distinguished Lecturer for the years 2015-2020, and AESS VP Publications (2018-2020). She is now IEEE SPS Director-at-Large for Region 8.

Her general interests are in the areas of statistical signal processing, estimation and detection the-

100 IEEE RADARCONF 2021 14 RADAR SUMMER SCHOOL ory. In particular, her research interests include clutter models, coherent and incoherent detection in non-Gaussian clutter, CFAR techniques, radar waveform diversity and bistatic/mustistatic active and passive radars, cognitive radars. She co-authored many book chapters and about 200 journal and conference papers.

Dr. Braham Himed received his Engineer Degree in electrical engineering from Ecole Nationale Polytechnique of Algiers, Algeria in 1984, and his M.S. and Ph.D. degrees both in electrical engineering, from Syracuse University, Syra- cuse, NY, in 1987 and 1990, respectively. Dr. Himed is a Division Research Fel- low with the Air Force Research Laboratory, Sensors Directorate, Multi-Spectral Sensing and Detection Division, Distributed RF Sensing Branch, in Dayton Ohio, where he is involved with several aspects of radar developments. His research interests include detection, estimation, multichannel adaptive signal processing, time series analyses, array processing, adaptive processing, waveform diversity, MIMO radar, passive radar, and over the horizon radar. Dr. Himed is the recipient of the 2001 IEEE Region I award for his work on bistatic radar systems, algorithm development, and phenomenology. He is a Fellow of the IEEE (Class of 2007) and a past-Chair of the AESS Radar Systems Panel. He is the recipient of the 2012 IEEE Warren White award for excellence in radar engineering. Dr. Himed is also a Fellow of AFRL (Class of 2013).

Marco Martorella is the Director of the CNIT’s National Radar and Surveillance Systems Laboratory, an Associate Professor at the University of Pisa and an external Professor at the University of Cape Town. He is author of more than 200 international journal and conference papers, 3 books and 17 book chapters. He is a member of the IEEE AES Radar Systems Panel, a member of the NATO SET Panel and a member of the EDA Radar Captech. He has been recipient of several awards, including the IEEE 2013 Fred Nathanson Memorial Radar Award. He is a Fellow of the IEEE.

Willie Nel is a principal radar researcher at the Council for Scientific and In- dustrial Research in South Africa and also appointed in the role of Technology and Innovation Manager for the radar research group. He holds MSc in Digi- tal Image Processing from the University of Cape Town and has been working passionately in the field of radar since 1999. His areas of expertise include radar system design, radar imaging, target recognition and radar signal processing. Current area of focus is the design and development of satellite and UAV SAR systems. He has published several papers in the area of radar target recognition and radar imaging and acts as a reviewer for several radar journals and conferences in this capacity. He has been participating in NATO workgroups in imaging since 2014. In 2015 he was technical chair of the IEEE radar conference when it was held in South Africa for the first time. He serves on the IEEE AESS Radar Systems Panel, is registered as anIEEEAESS mentor, currently chairs the IEEE Dennis J. Picard medal committee and is a member of the IEEE AESS Nathanson award committee. Most of all – he enjoys learning more about the infinite field of radar!

101 IEEE RADARCONF 2021 14 RADAR SUMMER SCHOOL

Robert “Bob” Palmer has decades of experience as an academic thought leader. Current, he holds the Tommy C. Craighead Chair in the School of Mete- orology at OU and is an Associate Vice President for Research & Partnerships. He also established and is Executive Director of the nationally recognized Ad- vanced Radar Research Center http://arrc.ou.edu.

Bob received the Ph.D. degree in electrical engineering from the University of Oklahoma, Norman, in 1989. From 1989 to 1991, he was a JSPS Postdoctoral Fellow with the Radio Atmospheric Science Center, Kyoto University, Japan, where his major accomplishment was the development of novel interferometric radar techniques for studies of atmospheric turbulent. After his stay in Japan, Dr. Palmer was with the Physics and Astronomy Department of Clemson University, South Carolina. From 1993 to 2004, he was a part of the faculty of the Department of Electrical Engineering, University of Nebraska, where his interests broadened into areas including wireless communications, remote sensing, and pedagogy. While at OU, his research interests have focused on the application of advanced radar signal processing techniques to observations of severe weather, particularly related to phased-array radars and other innovative system designs. He has published widely in the area of radar remote sensing of the atmosphere, with over 110 peer-reviewed journal articles, 1 textbook, 40 international invited talks, and over 300 conference presentations. His research has an emphasis on generalized imaging problems, spatial filter design, and clutter mitigation using advanced array/signal processing techniques.

Bob is a Fellow of both the American Meteorological Society and the Institute of Electrical and Elec- tronics Engineers and has been the recipient of several awards for both his teaching and research accomplishments.

Dr. Mike Picciolo is the Director of Mission Engineering in the Engineering, Integration and Logistics Division at SAIC. Previously he served as Chief Tech- nology Officer, NSS Division, at ENSCO. Prior, he was the Associate Chief Tech- nologist for Dynetics and Chief Engineer of the Advanced Missions Solutions Group in Chantilly, VA. He has in-depth expertise in Radar, ISR systems, Space Time Adaptive Processing and conducts research in advanced technology development programs. Has deep domain expertise in SAR/GMTI radar, communications theory, waveform diversity, wireless communications, hyperspectral imagery, IMINT, SIGINT, and MASINT intelligence disciplines. He is a member of the IEEE Radar Systems Panel, received the 2007 IEEE Fred Nathanson Radar Engineer of the Year Award, the 2018 IEEE AESS Outstanding Organizational Leadership Award, and founded the IEEE Radar Summer School series.

Simon Watts was a deputy Scientific Director and Technical Fellow in Thales UK until 2013 and is a Visiting Professor in the department of Electronic and Electrical Engineering at University College London. He graduated from the Uni- versity of Oxford in 1971 with an MA in Engineering Science, obtained an MSc in Radiocommunication and Radar Technology from the University of Birming- ham in 1972 and a PhD from the CNAA in 1987. In 2013 he was also awarded a DSc by the University of Birmingham, for research on sea clutter modelling. He joined Thales (then EMI Electronics) in 1967 and worked on a wide range of radar and EW projects, with a particular research interest in airborne mar-

102 IEEE RADARCONF 2021 14 RADAR SUMMER SCHOOL itime radar and sea clutter. He is author and co-author of over 80 journal and conference papers, a book on sea clutter (2 editions), various book chapters on clutter and several patents. He has also published two books on the history of airborne maritime surveillance radar. Simon Watts was chairman of the international radar conference RADAR-97 in Edinburgh UK and has served as a member of the IEEE AES Radar Panel and as an Associate Editor for Radar for the IEEE Transac- tions on Aerospace and Electronic Systems. He received the IEE JJ Thomson Premium Award in 1987, the IEE Mountbatten Premium Award in 1991 and the IEEE AES Warren White Award in 2020 for “contributions to airborne maritime surveillance radar design and the modelling of radar sea clutter”. He was appointed MBE by HM the Queen in 1996 for services to the UK defence industry and is a Fellow of the Royal Academy of Engineering, Fellow of the IET, Fellow of the IMA and Fellow of the IEEE.

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103 IEEE RADARCONF 2021 15 ABSTRACTS

15 Abstracts

Abstracts are grouped by session and ordered by their start time and location. For the reader’s convenience, a keyword index is provided at the end of this document.

1. Special Session: 20 Years of Waveform Diversity: Progress Toward Realizable Systems 2. Adaptive Signal Processing 3. Antennas and Components 4. Array Processing 5. Automotive Applications of Radar 6. Special Session: Biomedical and e-Healthcare Applications of Radar 7. Clutter and Target Signatures 8. Cognitive Radar & Machine Learning 9. Special Session: Deep Learning and AI for Radar (Part I) 10. Special Session: Deep Learning and AI for Radar (Part II) 11. Special Session: Digital Array Radar 12. ECCM & Interference Cancellation 13. Estimation and Detection in Challenging Target Scenarios 14. Estimation and Detection in Clutter and Interference 15. Ground-Penetrating and Sounding 16. Information Extraction from SAR images 17. MIMO Radar Techniques 18. Special Session: Machine Learning for Future Radar Technology 19. Medical and Biological Applications 20. Multichannel and Multistatic Passive Radar 21. Multistatic and Distributed MIMO systems 22. Special Session: Multistatic and Networked Radar - a Tribute to Viktor Chernyak 23. Special Session: Next-Gen Automotive Radars : Opportunities and Challenges 24. Passive Radar Applications 25. Special Session: Quantum Radar Theory and Practice (Part I) 26. Special Session: Quantum Radar Theory and Practice (Part II) 27. Radar Imaging 28. Radar Recognition Techniques and Applications 29. Resource Management 30. Special Session: Short-Range Radar Applications to Security 31. Software Defined Radar & Low-cost radar 32. Special Session: Spaceborne SAR Missions: State of the Art and Future Developments 33. Spectrum Sharing 34. Special Session: Synergistic Radar Signal Processing and Tracking 35. Synthetic Aperture Radar Imaging 36. Target Localization and Classification at short ranges 37. Terahertz & mmWave Radar 38. Tracking and Fusion 39. UAV Detection and Classification 40. Waveforms & Waveform Diversity

104 IEEE RADARCONF 2021 15 ABSTRACTS

TUESDAY

Estimation and Detection in Challenging Target Scenarios Begins: 5/11/2021 11:40 Ends: 5/11/2021 13:20 Location: Virtual Room A Chaired by Francesca Filippini and Hongbin Li

Analyzing the Effective Coherent Integration Time for Space Surveillance Radar Processing Rajat Awadhiya, Risto Vehmas

Keywords: Coherent Integration, RCS Fluctuations, Space Surveillance

This paper presents a practical approach to calculating the coherent processing interval (CPI) length for a space surveillance scenario. Our approach considers the fluctuating RCS and the dynamic trajectory of a target in the low Earth orbit. We simulate the target using a 3D CAD model and model its trajectory using a Keplerian orbit. The target RCS is estimated using physical optics method via POFACETS 4.1 software. We calculate the CPI length based on the RCS correlation width and the accuracy of the target motion model by demanding that the maximum possible phase error remains below an allowed tolerance.

Entropy-Based Coherent Integration Method for Moving Target Detection Using Phased-MIMO Radar Mingxing Wang, Xiaolong Li, Tao Fan, Zhi Sun, Chenyu Wang, Guolong Cui

Keywords: Coherent Integration, Multiple-Input Multiple-Output (MIMO) Radar, Entropy, Phase Compensation

In this paper, a coherent integration approach is presented for maneuvering target detection in phased-multi-in multi-out (MIMO) radar. The algorithm constructs a interesting cost function and uses the entropy as optimization criterion, dealing with phase compensation problem of multi- subarray echoes. This technique is not only capable of eliminate phase differences, but also does- nt́ require the knowledge about Doppler channels of target. Finally, numerical simulations demonstrate the validity of the proposed algorithm.

Statistics of Vehicular Detectability for Cooperative Passive Coherent Location at Urban Crossroad Saw James Myint, Steffen Schieler, Christian Schneider, Wim Kotterman, Giovanni Del Galdo, Reiner Thomä

Keywords: CPCL, JCRS, Bistatic RCS, Target Reflectivity, Target Fluctuation Model, Probability Of Detection

The statistical study of target detectability is crucial in radar system design. In this regard, signal- to-noise ratio (SNR) of the received signal at the radarś receiver is a key parameter and therefore every influence on SNR should be taken into account. One of the main influences is thefluctuation of the target scattering/reflectivity to the incident electromagnetic wave. Consequently, the target fluctuation models and their accuracy become important. For monostatic radar system, various

105 IEEE RADARCONF 2021 15 ABSTRACTS models can be found in the literature. In contrast, this paper studies a case of vehicular sensing scenario with multistatic configuration for an urban crossroad and proposes a modelling method of the target fluctuation by taking into account the target information, such as size, shape andcom- posed materials, and the wave information, such as polarizations and aspect angles. Furthermore, the derived target fluctuation models are then applied directly to the formulation of the probability of detection for multistatic radar system. These probabilities of detection are also compared to that of monostatic radar system.

A Novel Signal Power Based Multi-Targets Detection for FMCW Radar Yuki Tachibana, Chenggao Han

Keywords: FMCW Radar, Triangular Waveform, Chirp Waveform, Multi-Targets Detection

The frequency modulated continuous wave (FMCW) radar with a triangular waveform is widely applied in single target detection. However, in the multi-targets scenario, the FMCW radar yields ghost targets and many efforts against the issue are focused on the modified waveform designs. In this paper, we propose a novel signal power based multi-target detection in which we attempt to pair the beat frequencies from the same target by reference to the corresponding signal powers. Comparing with modified waveform based multi-target detections, the proposed method achieves higher estimation accuracies of both range and velocity with much lower computational complexity.

Going Below and Beyond Off-the-Grid Velocity Estimation from 1-Bit Radar Measurements Gilles Monnoyer de Galland, Thomas Feuillen, Luc Vandendorpe, Laurent Jacques

Keywords: Radar, Compressive Sensing, One-Bit, Off-The-Grid, Continuous Matching Pursuit

We propose to bridge the gap between using extremely low resolution 1-bit measurements and estimating targetsṕarameters that exist in a continuum by performing Off-the-Grid estimation. To that end, a continuous version of Orthogonal Matching Pursuit is modified in order to leverage the 1-bit measurements coming from a simple Doppler Radar. We show that although the resolution of the acquisition is dramatically reduced, detection of multiple targets can still be achieved and reaches performances beyond classic on-the-grid methods. Furthermore, we show empirically that adding a random and uniform dithering before the quantization is necessary when estimating more than one target.

Special Session: 20 Years of Waveform Diversity: Progress Toward Realizable Systems Begins: 5/11/2021 11:40 Ends: 5/11/2021 13:20 Location: Virtual Room B Chaired by Shannon Blunt and Eric Mokole

Waveform Design for Sparse Signal Processing in Radar Laura Anitori, Joachim Ender

Keywords: Waveform Design, Compressive Sensing, MIMO, Detection

In the past decades, there has been an extensive research interest in the areas of both waveform diversity/design and advanced signal processing algorithms departing from the more classical so-

106 IEEE RADARCONF 2021 15 ABSTRACTS lutions based on Linear Frequency Modulated (LFM) pulses and Matched Filters (MF). In the waveform diversity community, especially within the context of spectrum sharing, MIMO and cognitive radars, several waveform optimization and design methodologies have been studied, see [1], [2] and references therein. In parallel to waveform design, several signal processing techniques have also been proposed which exploit some kind of prior knowledge and/or iterative algorithms to improve the performance of the more classical MF, such as the Adaptive Matched Filter [3], MUSIC [4], CLEAN [5] and Sparse Signal Processing (SSP) [6], [7]. In this paper we present some results and examples to show how the combination of waveform design with SSP can lead to improved performance in radar compared to the more classical approach.

Cognitive Radar for Waveform Diversity Utilization Anthony Martone, Alexander Charlish

Keywords: Cognitive Radar, Waveform Diversity, Spectrum Sharing, Metacognition, Radar Resource Management

In this paper we discuss the relationship between cognitive radar and waveform diversity. The cognitive decision making process selects the most appropriate waveform from the available set of diverse waveforms based on a perception of the current environment and scenario, thus creating a perception action cycle (PAC). This combination of waveform diversity and cognition does not just improve performance, it is becoming critical for realizing modern radar systems due to increasingly challenging operating environments. Therefore, the robust techniques proposed in this paper can be highly valuable for enabling enhanced PACs in radar systems.

Practical Effects in Radar Transmitters and Their Effect on Spectrum Hugh Griffiths

Keywords: Radar Signals, Radar Transmitters, Spectrum Management

Transmitters form an essential part of all radar systems. Although modern radar signal design techniques promise precise, wideband transmitted signals with excellent spectral containment and low range sidelobes, practical radar transmitters introduce distortion which mean that these ideals are difficult to realize. Practical Waveform Diversity Applications and Implementation Challenges John Stralka, Daniel Thomas

Keywords: Waveform Diversity, Waveform Notching, Matched Illumination, MIMO, Passive Radar, Waveform Generation, Mismatched Filtering, Coexistence, Noise Radar

While radar systems have always employed diverse waveforms, waveform diversity, as coined in 2002, touches on a variety of radar topics including both established and emerging technologies. Over time, a number of waveform diversity applications have matured to the point where they have become practical and implementable. Nevertheless, there remain significant challenges to full implementation of many applications of unique interest. This paper provides an overview of some practical waveform diversity applications and discusses challenges that are yet to be overcome.

107 IEEE RADARCONF 2021 15 ABSTRACTS

Matched Correlation of Linear and Non-Linear Frequency-Modulated Waveforms for Far-Field TDOA-DoA in the Context of MFRFS Josef Worms, Michael Kohler, Daniel O’Hagan

Keywords: Matched Correlation, LFM, NLFM

This paper presents a comparison between linear frequency modulated waveforms and non-linear frequency-modulated waveforms for dual-channel time difference of arrival (TDOA) applications to estimate the direction of arrival (DoA) of an impinging planar wavefront. Non-linear frequency- modulated waveforms are well suited for the determination of TDOA by cross-correlation due to their correlation properties with respect to their reduced sidelobes compared to linear frequency modulated chirps. The waveforms are examined for customized dual-channel processing consisting of matched filtering of the ndividual receive channels and subsequent cross-correlation of the matched filter outputs. This kind of processing is especially suitable for signal-to-noise ra- tios below 0 dB. This enables two-channel e.g. electronic support measures (ESM) receivers or a distributed arrangement of receiver nodes to perform direction of arrival estimation at low signal powers and with only two receive paths.

Special Session: Next-Gen Automotive Radars : Opportunities and Challenges Begins: 5/11/2021 11:40 Ends: 5/11/2021 13:20 Location: Virtual Room C Chaired by Igal Bilik and Sandeep Rao

Multipath Ghost Targets Mitigation in Automotive Environments Oren Longman, Shahar Villeval, Igal Bilik

Keywords: Automotive Radar, Multipath Mitigation

This work addresses the probability of detection problem and false alarm performance degradation in automotive radar scenarios with multipath induced ghost targets. A novel multipath mitigation method which evaluates correlation between the target and its hypothesized ghost tracks, is proposed. The performance of the proposed method is evaluated with simulations and by using collected radar measurements, in practical scenes of moving target vehicles in adjacency to flat reflective surface. Characterization of Some Interference Mitigation Schemes in FMCW Radar Sandeep Rao, Anil Mani

Keywords: Automotive Radar, FMCW Radar, Interference, Mutual Interference, Dithering

This paper characterizes interference mitigation schemes in FMCW radar. We consider mitigation techniques at two stages of the processing chain: (a) time-domain and (b) post range-FFT. Time-domain techniques operate on the ADC samples and are useful when the interferer and the victim have widely differing slopes. Post Range-FFT techniques are useful when the interferer and the victim nominally identical slopes. In both these cases we analyze the interference detection requirements and then characterize the efficacy of various techniques.

108 IEEE RADARCONF 2021 15 ABSTRACTS

Multiplexing of OFDM-Based Radar Networks David Werbunat, Fabio Sgroi, Christina Knill, Benedikt Schweizer, Benedikt Meinecke, Rossen Michev, Jürgen Hasch, Christian Waldschmidt

Keywords: Radar Network, Repeater, Frequency Division Multiplexing (FDM), Time Division Multiplexing (TDM), Compressed Sensing, OFDM, OFDM-MIMO Radar

Radar networks offer the opportunity of enhancing the radar sensing performance beyond the inherent limitations of single multiple-input multiple-output (MIMO) radars. Different locations of the individual sensor nodes allow for diverse observation angles on the targets. Moreover, in case of a coherent network, such as radar-repeater networks, the direction of arrival (DoA) capabilities are increased. However, a crucial point in exploiting the full potential of a sensor network is the separa- bility of the signals originating from the different sensor nodes during operation, while preserving reasonable radar sensing performance of the individual sensors and the network. In this paper multiplexing methods for an OFDM-based radar network are proposed and investigated. The first method combines traditional time and frequency multiplexing, while the second method applies a random assignment in combination with a compressed sensing-based signal evaluation. Both methods are compared with the traditional FDM multiplexing for MIMO-OFDM. For this purpose, performance criteria are proposed and the concepts are evaluated based on measurements and simulations. Radar Interference Mitigation Through Active Coordination Canan Aydogdu, Musa Furkan Keskin, Gisela K. Carvajal, Olof Eriksson, Hans Hellsten, Hans Herbertsson, Emil Nilsson, Mats Rydström, Karl Vanäs, Mustafa Mete

Keywords: ADAS, AD, Radar, Communication, Radar Communication, FMCW, OFDM, Interference

Intelligent transportation is heavily reliant on radar, which have unique robustness under heavy rain/fog/snow and poor light conditions. With the rapid increase of the number of radars used on modern vehicles, most operating in the same frequency band, the risk of radar interference becomes an important issue. As in radio communication, interference can be mitigated through coordination. We present and evaluate two approaches for radar interference coordination, one for FMCW and one for OFDM, and highlight their challenges and opportunities.

Sparse Step-Frequency MIMO Radar Design for Autonomous Driving Shunqiao Sun, Lifan Xu, Nathan Jeong

Keywords: Automotive Radar, Multi-Input Multi-Output (MIMO) Radar, Sparse Step-Frequency Waveform, Autonomous Driving, Interference Mitigation

To accommodate a high number of automotive radars operating at the same frequency band while avoiding mutual interference, we propose a sparse step-frequency waveform (SSFW) radar to synthesize a large effective bandwidth to achieve high range resolution profiles (HRRP). To mitigate high range sidelobes in the SSFW radars, we propose a joint sparse carriers selection and weighting approach, where the sparse carriers are first optimally selected via the particle swarm optimization (PSO) techniques, and then a weighting vector is optimized and applied such that the peak sidelobe level of range spectrum is minimized. As a result, targets with relatively small radar cross section are detectable without introducing high probability of false alarm. We extend the SSFW concept to multi-input multi-output (MIMO) radar by applying phase codes along slow-time to synthesize a

109 IEEE RADARCONF 2021 15 ABSTRACTS large virtual array aperture. Numerical simulations are conducted to demonstrate the performance of the proposed SSFW MIMO radar.

Special Session: Quantum Radar Theory and Practice (Part I) Begins: 5/11/2021 11:40 Ends: 5/11/2021 13:20 Location: Virtual Room D Chaired by Bhashyam Balaji and Fred Daum

When Should We Use Likelihood Ratio Target Detection with QTMS Radar and Noise Radar? David Luong, Bhashyam Balaji, Sreeraman Rajan

Keywords: Quantum Radar, QTMS Radar, Noise Radar, Detector Function, Likelihood Ratio, ROC Curve We analyze the potential application of a generalized likelihood ratio (GLR)-based detector function to quantum two-mode squeezing (QTMS) radars and standard noise radars. We give an expres- sion for the likelihood ratio (LR) in terms of the maximum-likelihood estimate of the correlation coefficient between the received and reference signals of the radar. Interestingly, we foundthat a previously-studied detector function outperforms the GLR detector, though not in all parameter regimes. This runs counter to the intuition, based on the Neyman-Pearson lemma, that the LR test is optimal. We discuss why the lemma does not hold in this particular case and why the search for detector functions for QTMS radars and noise radars remains open. However, the GLR detector is a good choice when the correlation coefficient is high, the number of integrated samples islow, and appropriate computational resources are available.

Simulating Quantum Radar with Brownian Processes Marco Frasca, Alfonso Farina

Keywords: Wiener Process, Quantum Noise, Noise Square Root, Entanglement, Quantum Radar

We present a set of stochastic differential equations, defined through Brownian processes, that are solved in the complex field. The corresponding Kolmogorov-Chapman equation of such processes is a Schroedinger-like equation and then, we can represent quantum noise corresponding to a specific system by solving stochastic differential equations through ordinary numerical algorithms, making their implementation easier. These numerical algorithms can be considered as a definition of the corresponding processes used to define them. An application to basic quantum radar is attempted with the scope to outline the calculation of ROC (Receiver Operating Characteristic).

Quantum Radar and Noise Radar Concepts Konstantin Lukin

Keywords: Quantum Radar, Noise Radar, Range Resolution, Stepped-Frequency Quantum Radar

Comparative analysis of Quantum Radar (QR) based upon quantum entanglement phenomenon and Noise Radar (NR) based upon classical coherence and correlation processing of random signals are presented in the paper. Common and distinguishing features of NR and QR are illustrated and discussed. Novel concept of Stepped-Frequency Quantum Radar (SF-QR) for design of QR with range resolution capability is suggested. FPGA based digital generation/simulation of éntan- gledśignals is proposed.

110 IEEE RADARCONF 2021 15 ABSTRACTS

Energetic Considerations in Quantum Target Ranging Athena Karsa, Stefano Pirandola

Keywords: Quantum Radar, Quantum Target Ranging, Quantum Channel Discrimination, Multiple Hypothesis Testing

While quantum illumination (QI) can offer a quantum-enhancement in target detection, its potential for performing target ranging remains unclear. With its capabilities hinging on a joint-measurement between a returning signal and its retained idler, an unknown return time makes a QI-based protocol difficult to realise. This paper outlines a potential QI-based approach to quantum target ranging based on recent developments in multiple quantum hypothesis testing and quantum-enhanced channel position finding (CPF). Applying CPF to time bins, one finds an upper-bound on theerror probability for quantum target ranging. However, using energetic considerations, we show that for such a scheme a quantum advantage may not physically be realised.

Optimal Quantum Radar vs. Optimal Classical Radar with Full Polarization Antennas Fred Daum, Arjang Noushin, Jim Huang

Keywords: Quantum Radar, Polarization, Belavkin-Zakai Equation, Particle Flow Filter, Entanglement, Wigner Function

We compute the optimal quantum radar detection performance relative to the corresponding optimal classical radar detection performance with full polarization antennas. We use the Belavkin- Zakai equation rather than the Schrödinger equation, because the latter does not have an explicit measurement model. Our calculation is consistent with the well known 6 dB quantum advantage at low photon flux per mode assuming no polarization entanglement of photons and no useoffully polarized antennas.

Estimation and Detection in Clutter and Interference Begins: 5/11/2021 14:20 Ends: 5/11/2021 16:20 Location: Virtual Room A Chaired by Stephanie Bidon and Batu Krishna Chalise

Improved Target Detection in Spiky Sea Clutter Using Sparse Signal Separation Malcolm Wong, Elias Aboutanios, Luke Rosenberg

Keywords: Maritime, Non-homogeneous Clutter, Target Detection, Sparse Separation

Sparse signal separation is a technique that has demonstrated good performance for maritime target detection where the clutter is dynamic and non-homogeneous. Morphological component analysis (MCA) is a powerful separation algorithm, but requires careful selection of the penalty parameter to work effectively. In this paper, new methods of determining the penalty parameter are proposed and evaluated. Additional processing steps are also combined with the MCA output to improve the overall detection performance in spiky sea clutter. The new algorithms are then demonstrated against a number of traditional coherent detection schemes and compared using a Monte-Carlo simulation with simulated sea clutter.

111 IEEE RADARCONF 2021 15 ABSTRACTS

Distributed GLRT-Based Detection of Target in SIRP Clutter and Noise Batu Chalise, Kevin Wagner

Keywords: Distributed GLRT-based Detection Of Target In SIRP Clutter And Noise

A generalized likelihood ratio test (GLRT)-based distributed consensus algorithm is proposed for target detection in a radar network with clutter, which is modeled as a spherically invariant random process. Each radar solves optimization problems to maximize local likelihood functions under null and alternate hypotheses. The neighboring radars share their local GLRT values and update them iteratively until a convergence is reached to achieve a consensus. For noiseless case, the algorithm achieves the global GLRT, whereas in noisy case, consensus is not achieved. The performance of the algorithm is shown in terms of detection probability for a given false alarm probability.

Optimal Target Detection for Random Channel Matrix-Based Cognitive Radar/Sonar Touseef Ali, Christ Richmond

Keywords: Likelihood Ratio Test (LRT), Average Likelihood Ratio Test (ALRT), Receiver Operating Characteristics (ROC), Radar Detection

Conventional techniques for characterizing clutter depend on covariance-based statistical modeling. This presents a disadvantage to cognitive radar/sonar since optimizing waveform design becomes highly nonconvex. Modeling the clutter and target responses via random transfer functions known as channel matrices simplifies this waveform optimization problem. The goal ofthis paper is to explore the optimal receive architectures for target detection that emerge when these channel matrices are modeled as deterministic, and then as random using a Ricean channel model. A likelihood ratio test (LRT) is derived yielding the wellknown coherent matched filter, and an average LRT (ALRT) test is derived using Bayesian integration. The detection performance of these receivers is assessed and compared via standard analyses yielding receiver operating characteristic (ROC) curves. It is shown that the optimal ALRT is not strictly a linear function of the data.

Exploiting the Phase of a Bio-Inspired Receiver Krasin Georgiev

Keywords: Echolocation, Bats, Target Resolution, Complex Responses, SCAT, BSCT

Modern radar should be able to detect targets in heavy clutter and to identify specific targets. Both tasks require the ability to resolve close scatterers in order to be able to differentiate the target, either from natural or man-made objects. The output of a bat auditory system model for two close target echoes was studied. It was shown how the phase of the signal can be used to improve the resolution performance. The improvement is expected for ultra wideband bat-like waveforms and confirmed with experiments with ultrasound.

Quantized Time Delay for Target Localization in Cloud MIMO Radar Zhen Wang, Qian He, Rick S. Blum

Keywords: Cloud MIMO Radar, Quantization, Time Delay, Parameter Estimation

In this work, target localization is studied for cloud multiple-input multiple-output (MIMO) radar. Unlike previous work which quantizes local received signals, to reduce the communication burden,

112 IEEE RADARCONF 2021 15 ABSTRACTS this paper proposes to quantize local time delays for cloud MIMO radar parameter estimation. At each local sensor, the time delay of the signal traveling from the transmitter and returning to the receiver is estimated and quantized prior to transmission to the fusion center. The target location is estimated using the quantized time delay estimates at the fusion center, and the corresponding Cramer-Rao bound (CRB) is derived. Compared with existing methods based on received signal quantization, the proposed time delay quantization-based method can provide enhanced performance. MIMO Radar Moving Target Detection in Clutter Using Supervised Learning Shabing Ye, Qian He, Xiaorui Wang

Keywords: Target Detection, Compound Gaussian Clutter, MIMO Radar, Supervised Learning, GLRT Detector

To detect a potential moving target in presence of clutter, the generalized likelihood ratio test (GLRT) may be computationally expensive, due to unknown target and clutter parameters. This paper uses supervised learning, which can have the computational complexity under control, for multiple-input-multiple-output (MIMO) radar target detection in combating clutter. Compound Gaus- sian clutter with unknown parameters is considered for generating synthetic data. The measurements at all receivers are fed to a fully connected network (FCN), where detection is viewed as a binary classification problem. It is shown that the FCN-based detector can outperform theGLRT detector in the tested cases.

Waveforms & Waveform Diversity Begins: 5/11/2021 14:20 Ends: 5/11/2021 16:20 Location: Virtual Room B Chaired by Frank Robey and Fabrizio Santi

A Waveform Independent Approach to Detecting Targets in Clutter with Coherent Nonidentical Pulses Byrant Moss, Terry Foreman

Keywords: Pulse Diversity, Nonidentical Pulses, Optimum Detecto

This paper presents novel signal processing that enables detection of targets in clutter with waveforms that may change pulse-to-pulse. The waveform changes are unconstrained as long as they are coherent. It is well known that standard processing (i.e., matched filtering followed by moving target detector (MTD)) is not suitable for NP processing due to the loss of clutter coherence caused by Range Sidelobe Modulation (RSM). A simulation of the optimum detector with nonidentical pulses (ODNP) is explored and its performance is compared with that of both standard processing with nonidentical pulses (SPNP) and the optimum detector with identical pulses (ODIP).

Waveform Selection for a Scanning Radar in the Maritime Environment Azam Mehboob, Luke Rosenberg, Kutluyil Dogancay, Brian Ng, Mike Hartas

Keywords: Coherent Detection, Noncoherent Detection, Sea Clutter, K-distribution, Scan-To-Scan Integration, Frequency-Agility

Detection of slow moving targets for a scanning radar in the maritime domain is typically performed

113 IEEE RADARCONF 2021 15 ABSTRACTS using noncoherent detectors with both pulse-to-pulse and/or scan-to-scan integration. Other processing options include coherent processing for slowly scanning radars and pulse-to-pulse frequency agility which decorrelates the clutter, but restricts the processing to non-coherent. This paper studies the impact of these processing options when operating in different environments and proposes a guide for selecting the waveform that optimises the detection performance.

Computationally Efficient Joint-Domain Clutter Cancellation for Waveform-Agile Radar Christian Jones, Brandon Ravenscroft, James Vogel, Suzanne Shontz, Thomas Higgins, Kevin Wagner, Shannon Blunt

Keywords: Moving Target Indication, MTI, Waveform Agility, Joint-Domain Processing, Computational Efficiency, Clutter Cancelation

Waveform agility provides greater design freedom through the generation of a coherent processing interval (CPI) of nonrepeating waveforms. However, doing so introduces coupling of the range and slow-time Doppler dimensions that hinders clutter cancellation if not addressed. Non-identical multiple pulse compression (NIMPC) is a joint-domain approach that solves this problem, though the high dimensionality incurs a prohibitive computational cost. Inspired by recent work that exploits the Toeplitz structure of NIMPC, here we take that notion even further, demonstrating the processing of measured data at a cost significantly lower than direct NIMPC application.

Minimum PSL Discrete-Phase Waveform Design with Length-Change Mismatched Filter Rujun Hu, Yi Bu, Xianxiang Yu, Guolong Cui, Zhengxin Yan

Keywords: Waveform Design, Peak Sidelobe Level, Discrete-Phase, Mismatched Filter, Primal–dual Type Algorithm

This paper designs the discrete-phase waveform and its mismatched filter with a desired autocorrelation function. The Peak Sidelobe Level (PSL) of auto-correlation is minimized as a figure of merit. Then a general optimization problem involving unimodular, energy and SNR loss constraints on the waveform is introduced. An iterative procedure based on Primal–dual Type algorithm is presented to tackle the resulting NP-hard optimization problem. In each iteration, the problem is decomposed into four subproblems with closed-form solutions. Finally, numerical results are provided to demonstrate that the proposed algorithm can design the waveform and mismatched filter with satisfying result.

A Formal Study of the Doppler Tolerance of Costas and Sudoku Waveforms Bill Correll Jr, Travis Bufler, Christopher N. Swanson, Ram Narayanan

Keywords: Doppler Tolerance, Frequency Hopping, Costas Array, Sudoku

We apply a new, formal measure of Doppler tolerance of Setlur et al. to frequency-hopping waveforms using Costas arrays, Sudoku-based permutations and random-chosen permutations to pre- scribe the transmit pattern. We form statistics of Doppler tolerance envelopes computed from thresholding narrowband ambiguity functions of these waveforms. As functions of temporal off- sets, the statistical profiles indicate that the Sudoku-based waveforms are noise-like and serve to rigorously quantify the differences in Doppler tolerances between Costas arrays across signal thresholds.

114 IEEE RADARCONF 2021 15 ABSTRACTS

Design of Constant Modulus Sequence Set with Good Doppler Tolerance via Minimizing WISL Hui Qiu, Tao Fan, Yi Bu, Xianxiang Yu, Guolong Cui

Keywords: Constant Modulus Sequence Set, Weighted Integrated Side Lobe Level, Multiple-Input Multiple-Output Radar, High Speed Targets Detection

This paper deals with the design of the constant modulus sequence set for MIMO radar in an effort to achieve the desired correlation properties and favorable Doppler tolerance. The phase perturbation terms are forced in LFM waveform with different frequency modulation disturbance to achieve low correlation side lobe level. And the Weighted Integrated-Side-Lobe-Level as a figure of merit to minimize. Besides, the similarity constraint is involved. And the Coordinate Descent framework is introduced along with a quartic problem with a closed-form solution in each iteration. Numerical results prove the designed waveform set has good Doppler tolerance.

Automotive Applications of Radar Begins: 5/11/2021 14:20 Ends: 5/11/2021 16:20 Location: Virtual Room C Chaired by Kumar Vijay Mishra and Christian Waldschmidt

Code Diversity for Range Sidelobe Attenuation in PMCW and OFDM Radars Marc Bauduin, André Bourdoux

Keywords: PMCW Radar, OFDM Radar, Digitally Modulated Radars, APS, M-sequence

In this paper, we propose a solution to improve the range profile quality of digitally modulated radars. Such radars are able to offer high resolution combined with high sensitivity due to their ability to produce range profiles with low or zero sidelobes. Unfortunately, it has been shown that radar front-end non-linearities and Doppler shifts degrade the range profile by increasing the range sidelobes and/or producing ghosts. To significantly improve the range profile quality, we propose a system level solution based on a proper selection and use of the code sequences, thus relying on sequence families with a large set. We demonstrate by simulation the effectiveness of our

Automotive Synthetic Aperture Radar Imaging Using TDM-MIMO Masoud Farhadi, Reinhard Feger, Johannes Fink, Thomas Wagner, Andreas Stelzer

Keywords: TDM MIMO Radar, Automotive SAR, Image Formation, Velocity Ambiguity, Random Activation

Using multiple-input multiple-output (MIMO) structure for synthetic aperture radar (SAR) image formation brings advantages such as azimuth or elevation beamforming with small aperture size. Due to the simplicity of time-division multiplexing (TDM), it became a widely used approach for 77-GHz automotive radar systems. However, this method brings some limitations for platform movement and represents particular drawbacks in the reconstructed images. This paper tries to eliminate re- strictions and reduce artifacts using a random antenna activation pattern. The proposed idea leads to significant improvements for various simulation scenarios for our defined antenna structure.

115 IEEE RADARCONF 2021 15 ABSTRACTS

Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Driving Anthony Ngo, Max Paul Bauer, Michael Resch

Keywords: Radar Simulation, Sensor Modeling, Automotive Radar, Radar Point Cloud Classification, Virtual Validation, Neural Network, Deep Learning

The usage of environment sensor models for virtual testing is a promising approach to reduce the testing effort of autonomous driving. However, a method for quantifying the fidelity of a sensor model does not exist and the problem of defining an appropriate metric remains. In this work, we train a neural network to distinguish real and simulated radar sensor data with the purpose of learning the latent features of real radar point clouds. Furthermore, we propose the classifierś confidence score for the ‘real radar point cloudćlass as a metric to determine the degree offidelity of synthetically generated radar data. We have shown that the proposed deep evaluation metric outperforms conventional metrics in terms of its capability to identify characteristic differences between real and simulated radar data. Parallelized Instantaneous Velocity and Heading Estimation of Objects Using Single Imaging Radar Nihal Singh, Dibakar Sil, Ankit Sharma

Keywords: Velocity Estimation, Heading Angle, Parallel-Computing, Doppler Radar, Radar Signal Processing, Velocity Profile, GPU Kernel

The development of high-resolution imaging radars introduce a plethora of useful applications, particularly in the automotive sector. With increasing attention on active transport safety and autonomous driving, these imaging radars are set to form the core of an autonomous engine. One of the most important tasks of such high-resolution radars is to estimate the instantaneous velocities and heading angles of the detected objects (vehicles, pedestrians, etc.). Feasible estimation methods should be fast enough in real-time scenarios, bias-free and robust against micro- Dopplers, noise and other systemic variations. This work proposes a parallel-computing scheme that achieves a real-time and accurate implementation of vector velocity determination using frequency modulated continuous wave (FMCW) radars. The proposed scheme is tested against traffic data collected using an FMCW radar at a center frequency of 78.6 GHz and a bandwidthof4 GHz. Experiments show that the parallel algorithm presented performs much faster than its conventional counterparts without any loss in precision.

Adversarial Interference Mitigation for Automotive Radar Chenming Jiang, Tianyi Chen, Bin Yang

Keywords: Interference Mitigation, Radar, Adversarial Neural Network

With the massive application of radars in autonomous driving, mutual interference between radars has become a key issue. Over the past decades, many approaches have been proposed to solve this problem in time domain, frequency domain and space domain. These methods are mostly modelbased, whose performance may suffer from inaccurate modeling. In some works, the convolutional neural networks are applied to address this problem. In this paper, we propose to mitigate automotive radar interferences using convolutional neural networks with an adversarial framework. The performance of the model-based methods and the proposed learning-based method will

116 IEEE RADARCONF 2021 15 ABSTRACTS be compared.

Position and Velocity Fusion Using Multiple Monostatic Radar Sensors for Automotive Applications Christian Schüßler, Marcel Hoffmann, Randolf Ebelt, Ingo Weber, Martin Vossiek

Keywords: Radar Networks, Sensor Fusion, Kalman Filter, Automotive Radar

In order to protect vulnerable road users it is essential to detect and track them reliably and to predict their moving direction i.e. their velocity vector. A fusion approach for monostatic radar sensors is described which computes target positions and the assigned two dimensional instantaneous velocities. These fused detections are used to cluster extended objects and their position, velocity and covariance matrices are estimated. A converted measurement Kalman filter is utilized to track these detected objects.Test measurements using a setup of two separated radar stations demonstrate a considerably improved tracking performance compared to a single sensor setup.

Special Session: Quantum Radar Theory and Practice (Part II) Begins: 5/11/2021 14:20 Ends: 5/11/2021 16:20 Location: Virtual Room D Chaired by Bhashyam Balaji and Fred Daum

Quantum-Correlated Noise Radar with Phase-Sensitive Amplification Jonathan Blakely

Keywords: Quantum Radar, Phase-Sensitive Amplification, Parametric Amplifier, Bhattacharya Distance A form of quantum-correlated noise radar that uses phase-sensitive amplification is analyzed. Both the transmitted field and the retained reference field are subject to amplification of asinglefield quadrature. The receiver uses homodyne detection to correlate the quadratures of the retained field and received radiation. This approach is intended to avoid issues in previous implementations of quantum noise radar such as a weak signal field or quantum noise associated with phaseinsen- sitive amplification and heterodyne detection. The performance of quantum-correlated noise radar with phase-sensitive amplification is quantified in terms of the Bhattacharyya distance which sets a bound on the probability of detection errors in a symmetric detection scenario. The resulting bound is compared to some classical-state radar benchmarks. Despite making bestcase- sce- nario assumptions for the quantum radar and ignoring practical difficulties with implementation, no advantage over classical noise radar with a bright retained field appears in the Bhattacharyya distance. Microwave Quantum Radarś Alphabet Soup: QI, QI-MPA, QCN, QCN-CR Jeffrey Shapiro

Keywords: Quantum Radar, Entanglement, Target Detection, Receiver Operating Characteristic

Microwave quantum radar, in particular microwave quantum illumination, is receiving a great deal of research attention at this time. Some of that work, however, has led to hyperbolic summaries of quantum illumination’s potential, e.g., that it might compromise stealth aircraft. This paper dis- entangles microwave quantum radarś alphabet soup — quantum illumination (QI) radar, quantum

117 IEEE RADARCONF 2021 15 ABSTRACTS illumination with a microwave parametric amplifier receiver (QI-MPA) radar, quantum-correlated noise (QCN) radar, and quantum-correlated noise radar with a correlation receiver (QCN-CR) — by comparing their receiver operating characteristics to those of relevant classical-radar counterparts. The comparison indicates that QI target-detection has little to offer for radar sensing.

Quantum Radar - What Is It Good For? Robert Jonsson, Martin Ankel

Keywords: Quantum Radar

We question whether even a fully realized quantum radar can find use in any conventional radar setting, according to current understanding about its limitations and possibilities.

WEDNESDAY

Adaptive Signal Processing Begins: 5/12/2021 8:00 Ends: 5/12/2021 9:40 Location: Virtual Room A Chaired by Micheal Picciolo and Luke Rosenberg

Solution for Complex Amplitude in LCD Removal Algorithm Hanna Gjermo Chomitz, James Lievsay, Julie Ann Jackson

Keywords: Large Clutter Discrete, Clutter, STAP, Signal Processing, GMTI

Large clutter discretes (LCD) are spectrally bright localized clutter that can cause false alarms or missed target detections in space-time adaptive processing (STAP) data. For passive bistatic STAP, the LCD removal (LCDR) algorithm estimates the spatial/Doppler frequency and complex amplitude of the LCD and then removes it from the data. However, previous techniques to estimate the LCD’s amplitude were complicated and slow. This paper proposes a method that directly solves for the amplitude that minimizes the power output at the LCD’s spatial/Doppler frequency.

Knowledge-Aided Data-Driven Radar Clutter Representation Yi Feng, Chayut Wongkamthong, Mohammadreza Soltani, Yuting Ng, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Robert Calderbank, Muralidhar Rangaswamy, Vahid Tarokh

Keywords: Airborne Radar, RFView, Space-Time Adaptive Processing, Clutter Subspace Estimation, Virtual Clutter, Machine Learning, K-means Clustering

We use a knowledge-aided, data-driven, location-aware approach based on the RFView simulation software to model and estimate the effect of ground clutter in airborne radars. Using the RFView simulator, we produce many samples of potential clutter effects and, by employing the K-means clustering algorithm on the corresponding geographical power map given by RFView, represent them by a small number of virtual scatterers. These virtual scatterers are used for clutter estimation. We show comparable accuracy for significantly less computational time complexity to those of state-of-the-art methods using RFView realistic clutter-like data.

118 IEEE RADARCONF 2021 15 ABSTRACTS

Robust Adaptive Beamforming Based on the Direct Biconvex Optimization Modeling Xinying Zou, Qiping Zhang, Weijian Zhang, Jinfeng Hu

Keywords: Robust Beamforming, Biconvex Optimization, Approximation Errors, Alternating Direction Methods Of Multiplier

In general, robust adaptive beamforming (RAB) is modeled as a nonconvex optimization problem. The most of existing methods solve it indirectly by approximating the nonconvex problem to the convex optimization problem, which will cause the approximation error. Different from the existing methods, a novel method, which reformulates RAB as the biconvex form directly by use of an auxiliary variable, is proposed. Thus, the errors caused by the approximation can be avoided and the performance will be improved. Then the alternating direction methods of multiplier (ADMM) algorithm is applied to solve it. Compared with the existing methods, the results of the simulation experiments indicated that the proposed method has better performance encountered with types of signal steering vector mismatches.

Efficient Implementation of Iterative Adaptive Approach Based on GPU Framework forRadar Super-Resolution Imaging Jie Li, Yongwei Zhang, Yongchao Zhang, Deqing Mao, Yulin Huang, Jianyu Yang

Keywords: Iterative Adaptive Approach, Airborne Forward-Looking Radar, Super-Resolution Imaging, GPU Framework, Parallel Implementing

Iterative adaptive approach (IAA) is an effective way to improve the resolution of airborne forward- looking radar imagery. Regretfully, the computational complexity of autocorrelation matrix inversion is too high to reach the real-time technical requirement of radar imaging. In response to this problem, an efficient IAA method based on the graphics processing unit framework (IAA-GPU framework) is proposed in this paper which utilizes the multi-core, parallel, and multi-threaded characteristics of GPU framework to perform IAA processing. Compared with conventional implementation based on CPU framework, the proposed IAA-GPU framework enjoys a preferable computational efficiency without performance degradation. The method of implementing parallel IAA computation based on the GPU framework will be explained in detail, and simulations are given to verify the performance gain.

Enhancing Space-Time Adaptive Processing Through the Slepian Transform Lisa Osadciw, Daniel Hebert

Keywords: STAP, Adaptive Signal Processing, Direct Prolate Spherical Sequence (DPSS), Slepian Transform, Wideband Transform

Slepian Space Time Adaptive Processing improves cancellation of large interference sources by using the Slepian Transform to represent more clutter energy than other STAP approaches improving cancellation. STAP’s efficacy relies on its ability to estimate clutter interference across space (antenna elements) and time (pulses) but is limited by the interference strength and spectrum spread. In addition to limiting the clutter cancellation, the target signal may suffer STAP processing losses. This paper focuses on using signal to clutter enhancement metrics to capture this trade in the paper’s analyses. S-STAP uniquely transforms the covariance matrix using Slepian basis functions, which match the signals’ time and bandlimited nature. S-STAP uses a Fast Slepian transform (FST) from Karnik, et. all [1], that results in a very practical form of this processing. This article

119 IEEE RADARCONF 2021 15 ABSTRACTS demonstrates S-STAP removes more clutter than the standard sample matrix inversion (SMI) with losses below the Reed, Mallet, and Brennon (RMB) rule.

Special Session: Deep Learning and AI for Radar (Part I) Begins: 5/12/2021 8:00 Ends: 5/12/2021 9:40 Location: Virtual Room B Chaired by Sevgi Zubeyde Gurbuz and Graeme Smith

Quality of Service Based Radar Resource Management Using Deep Reinforcement Learning Sebastian Durst, Stefan Brüggenwirth

Keywords: Radar, Resource Management, Deep Reinforcement Learning

An intelligent radar resource management is an essential milestone in the development of a cognitive radar system. The quality of service based resource allocation model (Q-RAM) is a framework allowing for intelligent decision making but classical solutions seem insufficient for real-time application in a modern radar system. In this paper, we present a solution for the Q-RAM radar resource management problem using deep reinforcement learning considerably improving on runtime performance. Human Micro-Doppler Signature Classification in the Presence of a Selection of Jamming Signals Dilan Dhulashia, Matthew Ritchie, Shelly Vishwakarma, Kevin Chetty

Keywords: Micro-Doppler, Synthetic Jamming, Radar Classification, Radar Machine Learning

This work investigates the degradation effects of four distinct jamming signal styles on human micro-Doppler signatures by examining the ability of a linear discriminant classifier to accurately distinguish signatures collected using a simulated frequency modulated continuous wave radar which have been injected with the jamming signals. Misclassification dependence on jamming signal power for each jamming style is presented along with the nature of misclassifications.

Complex-Valued Neural Networks for Synthetic Aperture Radar Image Classification Theresa Scarnati, Benjamin Lewis

Keywords: SAR, Complex Networks, Machine Learning, ATR

SAR is an imaging modality used for a variety of military and civilian tasks, many of which could benefit greatly from computer automation. The increase in machine learning-based computer vision techniques in recent years has created a number of helpful methods to advance this goal, but most of these algorithms are designed for RGB imagery. Applying these off-the-shelf algorithms to magnitude only SAR imagery has shown promise in tasks such as ATR. However, very few algorithms exploit the complex-valued nature of radar data. We present a survey of several complex neural network techniques as applied to a SAR data set consisting of military targets. We comment on the merits of each approach and demonstrate the accuracy of each technique when 1) training data are limited, and 2) when the training and testing data exhibit a domain mismatch.

120 IEEE RADARCONF 2021 15 ABSTRACTS

Fool the COOL - on the Robustness of Deep Learning SAR ATR Systems Simon Wagner, Chandana Panati, Stefan Brüggenwirth

Keywords: Classification Systems, Convolutional Networks, Deep Learning, Automatic Target Recognition

Over the last years, deep learning automatic target recognition systems have become very popular for synthetic aperture radar images. These systems achieve very high classification rates with common datasets, like the Moving and Stationary Target Acquisition and Recognition (MSTAR) data. A point that is normally not considered is the robustness of these systems, which typically use a softmax layer without rejection class for classification. It has been reported in the past that small variations in the training and test data of deep neural networks might lead to a change in the result. To avoid this situation, several methods to increase the robustness are presented in this paper. These methods vary from simple, like training with noisy samples, to changes in the network structure, particularly the Competitive Overcomplete Output Layer (COOL) is proposed. The COOL gives an output value that also represents a confidence, but with a larger variation than the softmax output. To evaluate the robustness, the DeepFool algorithm is used to creates adversarial examples from known training data with the different networks.

Deep Learning Based Phaseless SAR Without Born Approximation Samia Kazemi, Birsen Yazici

Keywords: Deep Learning, Phase Retrieval, Image Reconstruction, SAR

In this paper, we present a phase retrieval approach from intensity measurements using a Deep Learning (DL) based Wirtinger Flow (WF) algorithm for the case where the measurement model is non-linear, and this non-linearity depends on the unknown signal. In the context of synthetic aperture radar (SAR), this is relevant to the image reconstruction problem for the scenario where the Born approximation is no longer valid which results in multi-scattering effect within the extended target being imaged. Since we are adopting WF for DL based imaging, the underlying optimization problem is non-convex. However, unlike the WF algorithm, the unknown image is estimated from the measurement intensities in a learned encoding space with the goal of achieving effective reconstruction performance. The overall DL network is composed of an encoding network for determining a suitable initial value in the transformed space, a recurrent neural network (RNN) that models the steps of a gradient descent algorithm for an optimization problem, and a decoding network that can incorporate the generative image prior and transforms the encoded estimation from the RNN output to the original image space.

UAV Detection and Classification Begins: 5/12/2021 8:00 Ends: 5/12/2021 9:40 Location: Virtual Room C Chaired by Carmine Clemente and Mark Govoni

A Comparison of Convolutional Neural Networks for Low SNR Radar Classification of Drones Holly Dale, Christopher J. Baker, Michail Antoniou, Mohammed Jahangir, George Atkinson

Keywords: Classification, Deep Learning, Staring Radar, Uavs, Birds

Convolutional neural networks have been shown to achieve high classification performance in bird-

121 IEEE RADARCONF 2021 15 ABSTRACTS drone classification, but results are only reported for high signal to noise ratio data in the literature. In this paper, a convolutional neural network is trained on radar spectrograms. Gaussian noise is added to the test data to vary the signal to noise ratio. Classifier robustness is then investigated as a function of SNR. The performance of six CNN architectures previously established for computer vision applications are exploited and compared with each other to assess classification performance and robustness with network depth.

Neural Network Based Drone Recognition Techniques with Non-Coherent S-Band Radar Engin Kaya, Gulay Buyukaksoy Kaplan

Keywords: Radar, Drone, Classification, Neural Network, Convolutional Neural Network, Long Short Term Memory

In this study, we proposed two classification approaches for recognition of the flying drone usingS- band radar. The radar data including drone, ship and bird targets is collected in various scenarios. While both proposed classification methods utilize neural networks, the first one is trained with the features extracted from track information. The second technique is based on radar images, thus video classification methods are employed. Experimental results have shown that the image- based method has better correct classification performance for not only drones but also birds and ships with a lower false alarm rate.

Extraction and Analysis of Micro-Doppler Signature in FMCW Radar Soorya Peter, Vinod Reddy

Keywords: FMCW Radar, Micro-Doppler Signature, Radar Signal Processing

Micro-Doppler effect due to target micro-motions induce additional frequency components in the received radar signal. For targets with high frequency micro-motion we observe that the conventional extraction of micro-Doppler signature in Frequency Modulated Continuous Wave (FMCW) radar fails due to insufficient sampling frequency along the slow-time. In this paper, wefirstde- velop a signal model for FMCW radar in the presence of a target with micro-motions. We then propose an approach to extract high frequency micro-Doppler signatures for FMCW radar, and verify it using simulation studies and experimental results.

Small Drone Detection Using Airborne Weather Radar William Blake, Isaiah Burger

Keywords: Drone Detection, Airborne Weather Radar

This paper focuses on how small drones such as the DJI Phantom 3 and DJI Inspire could be detected using existing weather radar hardware already installed in many airplanes. Garmin’s GWX 80 airborne weather radar was used as the weather radar to demonstrate this potential. Only software modifications were made to the aviation certified hardware for the feasibility of this approach.

UAV Micro-Doppler Signature Analysis Using FMCW Radar Vinod Reddy, Soorya Peter

Keywords: Micro-motions, micro-Doppler Signature, FMCW Radar, UAV Identification, Drone Detection

122 IEEE RADARCONF 2021 15 ABSTRACTS

Micro-motions of various target parts, that give rise to micro-Doppler signatures, can be employed as unique identifiers for targets. Due to high rotation rate of the rotor blades in unmanned aerial vehicles (UAV), the micro-Doppler frequency variation is significantly high. In conjunction with the estimation of UAV position and speed, the study of micro-Doppler signature for target characterization is essential. Frequency modulated continuous-wave (FMCW) radar is identified to have the potential to perform both the activities. In this work, we first study the signal model of FMCW radar in the presence of UAV. We then present a new approach for the study of high frequency micro- Doppler signatures. Simulation example and experimental data show the efficacy of the approach for micro-Doppler signature analysis.

Terahertz & mmWave Radar Begins: 5/12/2021 8:00 Ends: 5/12/2021 9:40 Location: Virtual Room D Chaired by Marina Gashinova and Lam Nguyen

A Terahertz Radar Feature Set for Device-Free Gesture Recognition Liying Wang, Zongyong Cui, Yiming Pi, Changjie Cao, Zongjie Cao

Keywords: Terahertz Radar, Feature Extraction, Frame-Level, Gesture Recognition

This paper proposes a set of simple but effective features using Terahertz radar, specifically for device-free gesture recognition based on high resolution range profiles. Three types of 7 features are extracted, which contains the tracking features, directional features and behavioral features of gestures. The proposed method is evaluated on a dataset of 10 kinds of 5 pairs of frequently-used gestures based on 0.34 THz radar. The experiments demonstrate that these features can not only encode the morphological differences among various dynamic gestures, but are also sensitive to the direction of the moving gesture within a short time period. The results show that the proposed method achieves 95.5 % accuracy on frame-level gesture recognition.

Teragogic : Open Source Platform for Low Cost Millimeter Wave Sensing and Terahertz Imaging

Adrien Chopard, Frédéric Fauquet, Jing Shun Goh, Mingming Pan, Anton Simonov, Olga Smolyanskaya, Patrick Mounaix, Jean-Paul Guillet

Keywords: Terahertz Imaging, FMCW Radar, Open Source, Educative, Low Cost

Terahertz and millimeter waves technologies have followed to a decade of strong development, thanks to leading promising applications fields such as hyper-spectral imaging,non-destructive testing, and spectroscopy. However, the generally high cost of such systems limits their use to academic research laboratories or high added value industries. In this article, we introduce the early-stage results of a several tens times lower cost fully integrated imaging system, operating above 100 GHz, available as an open-source project

123 IEEE RADARCONF 2021 15 ABSTRACTS

Open Radar Initiative: Large Scale Dataset for Benchmarking of Micro-Doppler Recognition Algorithms Daniel Gusland, Jonas Myhre Christiansen, Børge Torvik, Francesco Fioranelli, Sevgi Zubeyde Gurbuz, Matthew Ritchie

Keywords: Open-Source, Dataset, Radar Platform

In this paper, we discuss an “open radar initiative” aimed at proposing a common framework for the generation of a shared dataset of uniform radar data, to be used by the research community for a variety of applications. The initiative will include a COTS radar platform, signal processing and open-source datasets to be used for algorithm development and benchmarking.

Effects of Reference Frequency Harmonic Spurs in FMCW Radar Systems Jingzhi Zhang, Sherif Ahmed, Amin Arbabian

Keywords: Dynamic Range, FMCW Radar, Frequency Multiplier, Harmonic Spur, Millimeter-Wave

Harmonic spurs in local oscillators can degrade the performance of millimeter-wave frequency- modulated continuous-wave radar systems that employ a large-ratio frequency multiplier for LO generation. In this paper, we analyze the effect caused by harmonic spurs on system performance. Undesired spurs show up alongside the beat-frequency component in the de-chirped intermediate frequency spectrum, which deteriorates the system dynamic range (DR). The dynamic range can be accurately predicted by our model, and the measurement results of a 24-GHz radar are presented as a proof of concept. For high-dynamic-range radars, a harmonic rejection ratio of DR/2 in dB- scale is required for a given dynamic-range budget. A design example is presented with a 55-dBc harmonic rejection ratio, and it offers a 100-dBc spurious-free dynamic range.

High-Resolution Drone-Borne SAR Using Off-the-Shelf High-Frequency Radars Ali Bekar, Michail Antoniou, Christopher J. Baker

Keywords: Drone SAR, mini-UAV SAR

Real-world, drone-borne SAR imaging using off-the-shelf radars transmitting at high-frequencies are examined. Drones, in general, are better suited to short-range applications of up to a few hun- dred meters in range. However, short-range operation leads to significant space-variant errors, limiting the number of prominent targets observable within the beam. We are presenting the concept of an algorithm capable of handling spatially variant motion errors, and we test the validity of our approach using a technology demonstrator. For the first time, we present short-range, fine- resolution imagery (6cmx32cm) of an extended target area using drones at 24 and 77 GHz using this approach.

124 IEEE RADARCONF 2021 15 ABSTRACTS

ECCM & Interference Cancellation Begins: 5/12/2021 9:40 Ends: 5/12/2021 11:20 Location: Virtual Room A Chaired by Danilo Orlando and Ram Raghavan

Electronic Protection Mitigation Techniques Against Transmit Waveform Shaped Noise Jammers Alex Feltes, Ric Romero

Keywords: Electronic Warfare, Electronic Protection, Electronic Attack, Noise Jammer, Transmit Waveform Shaped Jammer, Eigenjammer, Knowledge-Based Jammer

In this paper, we develop electronic protection (EP) receiver techniques to mitigate the false alarm and detection effects generated by transmit waveform shaped noise jammers (TWS-NJ). A TWS- NJ assumes apriori knowledge of signal spectral shape, thereby utilizing waveformś dominant bands in generating jammer noise. The bandwidths of the TWS-NJs are parameterized to reflect the effect of practical narrowband constraints. The performance effect of various jammers, including TWS eigenjammer, on detection performance is also investigated. To mitigate these jammers, we develop a generalized EP matched filter for this family of jammers and evaluate performance improvement.

Target Signature Extraction Using Truncated Singular Value Decomposition for Electronic Protection Heitor Albuquerque, Ric Romero

Keywords: Electronic Warfare, Radar, Electronic Attack, Coherent Jammer, Electronic Protection, Target Signature, Matched Filter, Fighter Aircraft And Vehicle T

Recent studies show that jamming suppression can be performed using modified matched filters that are matched to the transmit waveform - target response (TWTR). Our interest is to obtain bandlimited extended target impulse responses via time-domain analysis of backscattered signals from simulations and open field measurements. In other words, our goal is to extract the extended response-signature from an actual target. We propose to use truncated singular value decomposition to extract the target signatures from TWTR. In our application, the target signature is exploited to mitigate the effect of coherent jammer for electronic protection.

Detection and Mitigation of Mutual RFI in C-Band SAR : A Case Study of Chinese GaoFen-3 Zongsen Lv, Ning Li, Zhengwei Guo, Jianhui Zhao

Keywords: Mutual Radio Frequency Interference, Synthetic Aperture Radar, GaoFen-3 Satellite

In this paper, a new format of RFI, namely the mutual RFI (MRFI), which is a kind of scatter-wave RFI originating from the nearby ground area simultaneously illuminated by different SAR satellites, was investigated. In order to solve the issue of MRFI, a novel RFI detector based on spectrum energy cancellation (SEC) and a RFI mitigation technology based on improved eigen-subspace projection method were proposed. In this work, a systematic analysis of MRFI is performed, and the proposed mitigation process is introduced. Finally, experiments accomplished over GaoFen-3 single look complex data confirm the effectiveness of the proposed technique.

125 IEEE RADARCONF 2021 15 ABSTRACTS

Adaptable RF/Analog Transmit Leakage Canceller for Simultaneous Transmit/Receive Applications Peter Stenger, Raymond Power

Keywords: STAR, Adaptive RF/analog Cancellation, Digital Filtering, Duplexer, Dynamic Range, Direct RF, IBW

A software programmable digital domain filter supporting 250 MHz of instantaneous bandwidth (IBW) in a direct RF digital transceiver has demonstrated 25 dB of RF transmit leakage cancellation in a simultaneous transmit and receive mode at 3.0 GHz. The adaptively programmed cancellation signal is applied in the RF domain before the receive ADC increasing receiver dynamic range 25 dB while transmitting, allowing very small receive signals within the same band as the transmit band to be detected at the same time. Doppler Filter Bank Design for Non-Uniform PRI Radar in Signal-Dependent Clutter Tao Fan, Yukai Kong, Mingxing Wang, Xianxiang Yu, Guolong Cui, Liwei Zhang

Keywords: Non-uniform Pulse Repetition Interval, Range-Doppler Ambiguity, Clutter Suppression, Parallel Block Improvement

This paper deals with the design of the Doppler filter bank for non-uniform pulse repetition interval (PRI) radar in the presence of signal-dependent clutter. The worst-case Signal-to-Interference- Noise-Ratio (SINR) at the output of the Doppler filter bank is explicitly maximized along with the mainlobe gain restriction. To solve the resultant non-convex problem, a Parallel Block Improve- ment (PBI) algorithm is proposed to monotonically increase the worst-case SINR to convergence. In particular, each block involves a non-convex problem associated with the each filter, which is approximated through a series of convex problem. Finally, numerical simulations confirm the performance of the proposed algorithm.

Special Session: Deep Learning and AI for Radar (Part II) Begins: 5/12/2021 9:40 Ends: 5/12/2021 11:20 Location: Virtual Room B Chaired by Sevgi Zubeyde Gurbuz and Graeme Smith

Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-Cloud Data Peter Svenningsson, Francesco Fioranelli, Alexander Yarovoy

Keywords: Object Detection, Object Recognition, Radar, Geometric Deep Learning, nuScenes

This work proposes an object recognition model for radar detection data which imposes a graph structure on the radar point-cloud by connecting spatially proximal points and extracts local patterns by performing convolutional operations across the graphś edges. The model is evaluated on the nuScenes benchmark and is one of the first radar object recognition models evaluated ona public dataset. The results show that end-to-end deep learning solutions for object recognition in the radar domain are viable but currently not competitive with solutions based on LiDAR or camera data.

126 IEEE RADARCONF 2021 15 ABSTRACTS

Reinforcement Learning for Waveform Design Graeme Smith, Taylor Reininger

Keywords: Reinforcement Learning, Neural Networks, DSP, Waveform Design, Spectrum Sharing, Coexistance

In this paper we propose the use of a deep neural network (DNN) trained using reinforcement learning (RL) to solve a simplified radar waveform design problem. The problem is to design polyphase codes that result in a waveform with a power spectral density (PSD) containing a low power notch to support spectrum sharing. The use of an actor-critic approach to DNN learning removes the need for a training dataset and replaces it with a reward function based on conventional radar metrics. Currently, the approach provides a notch that is O(20 dB) deep, but with appreciable variation across the PSD. Continuing tuning of the RL meta-parameters promises improved performance for the final paper draft.

Through-Wall Human Activity Classification Using Complex-Valued Convolutional Neural Network Xiang Wang, Pengyun Chen, Hangchen Xie, Guolong Cui

Keywords: Complex-valued Convolutional Neural Network, Human Activity Classification, Range Profile

In this paper, the complex-valued convolutional neural network (Complex-valued CNN) is utilized to classify the human activity behind the wall. We developed several Complex-valued CNN models, which have the same structures as several classical convolutional neural network (CNN) models and use both the amplitude and phase information of the range profiles. Experiments on the real data validate the performance of the Complex-valued CNN models.

Sign Language Recognition Using Micro-Doppler and Explainable Deep Learning James McCleary, Laura Parra García, Christos Ilioudis, Carmine Clemente

Keywords: DSP DL BSL

In this paper, Sign Language Recognition and classification of the micro-Doppler signatures of different BritishSign Language (BSL) gestures is studied. A database of four different BSL hand gesture motions is presented in the form of micro-Doppler signals, recorded with a continuous waveform radar. For detecting the presence of the micro-Doppler signatures, joint time-frequency is applied. Each individual gesture is expected to contain unique spectral characteristics that are exploited in order to classify the gestures. A deep learning approach with transfer learning is studied and discussed for carrying out the classification task. Following this, a novel explainable AI algorithm is implemented to give the user visual feedback, in the form of colour highlights, for the mostrelevant features used to classify each signal.

Complex-Valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation Alexander Fuchs, Johanna Rock, Mate Toth, Paul Meissner, Franz Pernkopf

Keywords: Complex-valued Convolutional Neural Networks, Automotive Radar, Interference Mitigation, Denoising, Range-Doppler Processing, Deep Learning

127 IEEE RADARCONF 2021 15 ABSTRACTS

The increased use of radar sensors in road traffic introduces mutual interference between multiple radar sensors. This interference must be mitigated to ensure a high and consistent detection sensitivity. In this paper, we propose the use of Complex-valued Convolutional Neural Networks (CVCNNs) to address mutual interference between radar sensors. We extend previously developed methods to the complex domain increasing data efficiency and improving phase conservation during filtering, which is crucial for further processing. Our experiments show, that theuse of complex-valued CNNs increases data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.

Special Session: Short-Range Radar Applications to Security Begins: 5/12/2021 9:40 Ends: 5/12/2021 11:20 Location: Virtual Room C Chaired by Alessio Balleri and Victor Chen

An LSTM Approach to Short-Range Personnel Recognition Using Radar Signals Zhenghui Li, Julien Le Kernec, Francesco Fioranelli, Olivier Romain, Lei Zhang, Shufan Yang

Keywords: Radar Sensing, Personnel Recognition, LSTM Network, Phase Information, Micro-Doppler Signatures, Range-time Information

In personnel recognition based on radar, significant research exists on statistical features extracted from the micro-Doppler signatures, whereas research considering other domains and information such as phase is less developed. This paper presents the use of deep learning methods to integrate both phase and magnitude features from range profiles and spectrogram. The temporal features of both domains are separately extracted using a stack of Long Short Term Memory (LSTM) layers. Then, the extracted features are aggregated in the corresponding domains and pass through a series of dense layers with SoftMax classifier. Finally, the information from the two domains is fused with a soft fusion approach to improve the performance further. Preliminary results show that the proposed network with soft fusion can achieve 85.5% accuracy in personnel recognition with six subjects.

Enhanced Micro-Doppler Feature Analysis for Drone Detection Yimin Zhang, Xingyu Xiang, Yi Li, Genshe Chen

Keywords: Drone Detection, Unmanned Aerial Vehicle, Time-Frequency Analysis, Inverse Radon Transform

As low-cost drones become more accessible, they pose various safety, security, and privacy threats. As such, it becomes increasingly important to detect their presence, locate and track their positions, and classify their types in real time. In this paper, we perform time-frequency analyses of drone Doppler and micro-Doppler signatures to provide enhanced drone detection and feature extraction capabilities. The analyses are based on the combined use of spectrogram and inverse Radon transform (IRT). The paired property of propeller blades associated with a rotor is further utilized to compute the IRT product for enhanced performance. It is demonstrated that the IRT and IRT product images, when expressed in terms of the rotation frequency and blade position phase, provide flexibility and effectiveness for the presentation and estimation of these parameters.

128 IEEE RADARCONF 2021 15 ABSTRACTS

Harmonic Radar for Differentiating Between Friend and Foe Tanisha Gosain, Shobha Ram

Keywords: Harmonic Radar, Dictionary Learning, Search And Rescue

A modified harmonic radar architecture for distinguishing between a friend and a foe in thepropa- gation channel is presented. The main application is for search and rescue missions and surveillance. The radar consists of a narrowband sinusoidal transmitter and a dual channel receiver tuned to the fundamental transmitted frequency and its second order harmonic. The radar scattered returns from both friend and foe are gathered at the primary receiver while the secondary receiver gathers the second order harmonics emanating from the harmonic radar tag on the friend. Then dictionaries trained from the secondary receiver data are used to disaggregate and reconstruct the returns from the friend and foe in the primary linear radar data.

DVB-S Based Passive Radar for Short Range Security Application Francesca Filippini, Octavio Cabrera, Carlo Bongioanni, Fabiola Colone, Pierfrancesco Lombardo

Keywords: Passive Radar, DVB-S Signals, Short-Range Surveillance, Direction Of Arrival Estimation

DVB-S based passive radar have been shown to effectively detect small RCS targets in the short- range, providing high range and velocity resolution. However, the critical infrastructure protection application also requires: (i) an adequate angular measurement accuracy, to localize to targets in the Cartesian Plane, and (ii) a real-time processing capability, able to provide prompt and frequently updated monitoring. This works presents an architecture and a processing scheme able to provide these characteristics and demonstrates it by fielding, within Sapienza University of Rome, alow- cost COTS-based PCL receiver. The analysis of the experimental results validates the selected solution and demonstrates its high effectiveness.

Multi-Frequency RF Sensor Data Adaptation for Motion Recognition with Multi-Modal Deep Learning Mohammad Mahbubur Rahman, Sevgi Zubeyde Gurbuz

Keywords: micro-Doppler, Radar, Multi-Modal Learning, Adversarial Neural Networks

The widespread availability of low-cost RF sensors has made it easier to construct RF sensor networks for motion recognition, as well as increased the availability of RF data across a variety of frequencies, waveforms, and transmit parameters. However, it is not effective to directly use dis- parate RF sensor data for the training of deep neural networks, as the phenomenological differences in the data result in significant performance degradation. In this paper, we consider twoap- proaches for the exploitation of multi-frequency RF data: 1) a single sensor case, where adversarial domain adaptation is used to transform the data from one RF sensor to resemble that of another, and 2) a multi-sensor case, where a multi-modal neural network is designed for joint target recognition using measurements from all sensors. Our results show that the developed approaches offer effective techniques for leveraging multi-frequency RF sensor data for target recognition.

129 IEEE RADARCONF 2021 15 ABSTRACTS

Medical and Biological Applications Begins: 5/12/2021 9:40 Ends: 5/12/2021 11:20 Location: Virtual Room D Chaired by Fauzia Ahmad and Moeness Amin

Word-Level Sign Language Recognition Using Linguistic Adaptation of 77 GHz FMCW Radar Data Mohammad Mahbubur Rahman, Robiulhossain Mdrafi, Ali Cafer Gurbuz, Evie Malaia, Chris Crawford, Darrin Griffin, Sevgi Zubeyde Gurbuz

Keywords: ASL, Sign Language, Gesture Recognition, RFsensing, Radar, micro-Doppler, Deep Learning

This paper investigates the efficacy of RF sensors for word-level ASL recognition in support of human-computer interfaces designed for deaf or hard-of-hearing individuals. A principle challenge is the training of deep neural networks given the difficulty in acquiring native ASL signing data. In this paper, adversarial domain adaptation is exploited to bridge the linguistic gap between the copy signing of hearing individuals and native signing of fluent/deaf individuals. Domain adaptation results are compared with that attained by directly synthesizing ASL signs using generative adversarial networks(GANs). Kinematic improvements to the GAN architecture, such as the inser- tion of micro-Doppler signature envelopes in a secondary branch of the GAN, are utilized to boost performance. Word-level classification accuracy of %91.3 is achieved for 20ASL words.

Radar-Based Efficient Gait Classification Using Gaussian Prototypical Networks Usman Niazi, Souvik Hazra, Avik Santra, Robert Weigel

Keywords: Gait Analysis, Mm-Wave Radar, Prototypical Networks

Remote gait sensing and classification has potential applications in medical diagnosis, assisted living and recognition tasks. In this paper, we propose to classify human’s gait data extracted from radar exploiting the time-frequency representation of the micro-Doppler signatures. Gait recognition for two tasks, namely human identification and human with luggage type, are demonstrated in this paper that uses the cadencevelocity diagram (CVD) of the micro-Doppler time data as input to Gaussian prototypical network for classification. Gaussian prototypical networks learn the projection of CVD gait data into embedding vector along with covariance representing the confidence region around the embedding vector, which are then clustered for classification. We demonstrate the performance of our proposed solution with 8 individuals (33 ± 7 years) for the task of person identification and human walking types namely usual walking, walking with backpack andwalking with trolley.

Multiple Moving Targets Heartbeat Estimation and Recovery Using Multi-Frequency Radars Yu Rong, Kumar Vijay Mishra, Daniel W. Bliss

Keywords: Remote Sensing, UWB Radar, Multiple Heartbeat Detection, JPDA

In this paper, we present a novel non-contact and privacy preserving approach on recovering physi- ological measurements like heart rate when multiple subjects are slowly walking in the scene using a ultra-wideband (UWB) multi-frequency radar system. We employ joint probabilistic data associa-

130 IEEE RADARCONF 2021 15 ABSTRACTS tion (JPDA) algorithm to separate detection from multiple moving objects. Additionally, we exploit a recently developed multi-channel variational mode decomposition (MVMD) algorithm to extract heartbeat information from the range aligned samples. Our contribution lies in demonstration the possibility of a novel signal processing approach on pulse detection while multiple human targets are moving on the order of a few meters with a relatively simple setup while prior works requires a more complex setting and only considers small body movement on centimeter levels.

Sequential Classification of ASL Signs in the Context of Daily Living Using RFSensing Emre Kurtoglu, Ali Cafer Gurbuz, Evie Malaia, Darrin Griffin, Chris Crawford, Sevgi Zubeyde Gurbuz

Keywords: Sequential Classification, Trigger Detection, RF Sensing, Gesture Recognition

RF sensing based human activity and hand gesture recognition (HGR) methods have gained enor- mous popularity with the development of small package, high frequency radar systems and powerful machine learning tools. However, most HGR experiments in the literature have been conducted on individual gestures and in isolation from preceding and subsequent motions. This paper considers the problem of American sign language (ASL) recognition in the context of daily living, which involves sequential classification of a continuous stream of signing mixed with daily activities. The proposed approach involves first detecting and segmenting periods of motion, followed by feature level fusion of the range-Doppler map, micro-Doppler spectrogram, and envelope for classification with a bi-directional long short-term memory (BiLSTM) recurrent neural network. Results show 93.3% accuracy in identification of 6 activities and 4 ASL signs, as well as a trigger gesture detection rate of 0.93. Heartbeat Measurement with Millimeter Wave Radar in the Driving Environment Chris Schwarz, Hunza Zainab, Soura Dasgupta, Justin Kahl

Keywords: Millimeter Wave Radar, Non-Contact Vital Signs, Automotive, Driving

Millimeter wave radar has proven to be an effective method to measure human vital signs in a non-contact manner. However, the driving context is complex and noisy, and this fact must be considered in the algorithm design as well as in testing and verification methodologies. We implemented an estimation algorithm based on arctangent demodulation and added refinements to optimize performance. Three different algorithm choices, peak spectrum, MUSIC, and particle filter were tested while driving in an on-road vehicle as well as in a high-fidelity driving simulator. Accu- racy was explored over a parameter space by varying window size and error tolerance. We present results that help to select the method and parameter. Additionally, we conclude that a high-fidelity simulator is an appropriate environment to conduct radar tests for in-cabin monitoring.

MIMO Radar Techniques Begins: 5/12/2021 11:40 Ends: 5/12/2021 13:20 Location: Virtual Room A Chaired by Guolong Cui and Muralidhar Rangaswamy

Cognitive-Driven Optimization of Sparse Array Transceiver for MIMO Radar Beamforming Weitong Zhai, Xiangrong Wang, Syed A. Hamza, Moeness G. Amin

Keywords: Cognitive MIMO Radar, Two-Dimensional Group Sparsity, Mixed Reweighted L2, 1-Norm, MaxSINR Beamforming

131 IEEE RADARCONF 2021 15 ABSTRACTS

In this paper, we propose a novel cognitive MIMO radar concept where both the beamforming weights and the transceiver configuration are adaptively optimized under different environmental conditions. In the sensing step, the covariance matrix of the full virtual array is constructed by sequentially switching among different sets of antennas. In the learning step, the transmit and receive arrays with a given number of antennas are designed by antenna selection, satisfying practical constraint that transmitting/receiving antennas are not allowed to be overlapping or adjacent to each other for high isolation.

MIMO Radar Waveform Design via Deep Learning Kai Zhong, Weijian Zhang, Qiping Zhang, Jinfeng Hu, Pengfei Wang, Xianxiang Yu, Qiyu Zhou

Keywords: MIMO Radar, Constant Modulus Waveform Design, Signal-to-interference And Noise Ratio, Integrated Sidelobe Levels, Deep Learning

In this paper, we consider constant modulus waveform design with higher signal-to-interference and noise ratio (SINR) and lower integrated sidelobe levels (ISL). To further improve SINR, we co- here the transmit power to the target direction and put radiation nulls to interference directions. The resulting design is a non-convex optimization problem, then an algorithm based on deep learning (DL) is proposed. Numerical results show that our proposed algorithm has better performance than the recent introduced methods. A Novel Towed Jamming Suppression with FDA-MIMO Radar Siqi Li, Zhulin Zong, Yun Feng

Keywords: Adaptive Beam-Forming, Frequency Diverse Array, MIMO Radar, Towed Jamming

Frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) radar can form angle- distance 2D beam pattern, which has good anti-jamming ability. This paper proposes a towed jamming suppression method based on FDA-MIMO radar. The minimum variance spectral estimation (MVSE) algorithm is used to accurately locate the target and the jamming location, and then the target and the jamming are identified based on the difference in Doppler frequency. Finally, the minimum variance distortionless response (MVDR) adaptive beamforming algorithm suppresses jamming. In addition, this paper selects the square frequency increment instead of the commonly used linear frequency increment, so that the beam has a better range resolution to obtain a better anti-jamming effect. The simulation results prove the effectiveness of the method proposed in this paper.

New Coherent and Hybrid Detectors for Distributed MIMO Radar with Synchronization Errors Cengcang Zeng, Fangzhou Wang, Hongbin Li, Mark Govoni

Keywords: Distributed MIMO Radar, Non-Orthogonal Waveforms, Asynchronous Propagation, Timing, Frequency, And Phase Errors, Target Detection

We examine the impact of synchronization errors on target detection in distributed multi-input multi-output (MIMO) radar. As the sensors are spatially distributed, the waveforms undergo different propagation delays and Doppler frequencies and thus may lose mutual orthogonality. The problem becomes more severe in the presence of synchronization errors. In this paper, we propose a coherent detector that considers the cross correlation in phase compensation and exploits different signal strength among different transmit/receive paths. We also propose a hybrid detec-

132 IEEE RADARCONF 2021 15 ABSTRACTS tor as a trade-off solution, in terms of detection performance and synchronization requirement, to bridge coherent detection and non-coherent detection.

MIMO Radar Beampattern Formation with Spectral Coexistence via Sequential Convex Approximation Xianxiang Yu, Hui Qiu, Tao Fan, Yi Bu, Guolong Cui

Keywords: Multiple-Input Multiple-Output (MIMO) Radar, Beampattern, Integrate-Sidelobe-Level (ISL), Peak-to-Average Ratio (PAR)

We study the spectral constrained design of Multiple-Input Multiple-Output (MIMO) radar beampattern in an effort to the coexistence with the communication systems. We develop an optimization model relying on the minimization of beampattern Integrate-Sidelobe-Level (ISL) along with spectral, mainlobe width, Peak-to-Average Ratio (PAR) and energy constraints. To cope with the resultant non-convex problem, we introduce a novel polynomial-time iterative procedure that requires solving a series of constrained convex problems that evolve with each iteration. In particular, the proposed algorithm ensures that the monotonic decrease of ISL and the convergent solution is a KarushKuhnTucker (KKT) point.

Special Session: Machine Learning for Future Radar Technology Begins: 5/12/2021 11:40 Ends: 5/12/2021 13:20 Location: Virtual Room B Chaired by Anthony Martone and Justin Metcalf

Artificially Intelligent Power Amplifier Array (AIPAA): A New Paradigm in Reconfigurable Radar Transmission Charles Baylis, Robert J Marks II, Austin Egbert, Casey Latham

Keywords: Cognitive Radar, Power Amplifiers, Radio Spectrum Management, Spectrum Sharing

The increasing re-allocation of traditional radar bands for spectrum sharing requires future radar systems to be both adaptive and reconfigurable in real time. Radar array outputs must bemon- itored so that related inputs are adjusted to ensure spatial and spectral coexistence with other systems while maximizing performance. We discuss a new concept: an array of reconfigurable power amplifiers coupled with fast algorithms that can allow performance to be re-optimized upon changes in operating frequency or beam steering. This AIPAA will be capable of embedding artificial intelligence (AI) and machine learning (ML) techniques to optimize the array pattern with the waveforms and circuitry. The AIPAA will optimize the transmitter and transmissions to coexist within the spectral and spatial domains. A spatial-spectral mask and an approach to join different optimizations are discussed as useful building blocks for constructing the AIPAA optimizations. The impact of circuit linearity on the array pattern and potential improvement from real-time reconfigurable circuitry in the array elements are also discussed.

Multi-Player Bandits for Distributed Cognitive Radar William Howard, Charles Thornton, Anthony Martone, Michael Buehrer

Keywords: Radar Networks, Multi-Arm-Bandit, Cognitive Radar, Reinforcement Learning

With new applications for radar networks (e.g., automotive), the need for spectrum sharing and

133 IEEE RADARCONF 2021 15 ABSTRACTS general interoperability will continue to rise. This paper describes the application of multi-player bandit algorithms to a distributed cognitive radar network that must coexist with a communications system. Specifically, we assume that radar nodes in the network have no means ofdirectly communicating. The radar nodes attempt to optimize their own spectrum utilization while sharing spectrum with each other and the communication system. First we examine models that assume each node experiences equivalent average performance in each band, and later examine model that relaxes this assumption.

RADGAN: Applying Adversarial Machine Learning to Track-Before-Detect Radar Caleb Carr, Bibi Dang, Justin Metcalf

Keywords: Generative Adversarial Networks, Radar, Signal Processing, Target Detection

In this paper, the authors explore employing Generative Adversarial Networks (GANs) toward radar target detection. Current machine learning approaches focus on Convolutional Neural Networks (CNNs) for real-time detection. Most GANs do not require labeled data, and this paper will investigate how unsupervised learning methods fare at detecting targets compared to existing ones.

LTE Interference Effects on Radar Performance Jordan Devault, Jacob Kovarskiy, Benjamin Kirk, Anthony Martone, Ram Narayanan, Kelly Sherbondy

Keywords: Spectrum Sharing, Cognitive Radar, 4G LTE Communications, Metacognition, Interference Characterization, Mutual Interference

To facilitate the increased demand on the radio frequency spectrum, the federal communications commission (FCC) allows spectrum sharing between 4G long term evolution (LTE) communications and radar. A cognitive radar that employs metacognition and many dynamic spectrum access (DSA) techniques to successfully share the spectrum has been developed. This paper suggests metrics to be used to characterize these techniques in terms of mutual interference with the LTE communications. In particular, a Block Collison rate (BCR) metric is proposed to quantitatively analyze the effect of the radar on LTE communications from an outside observer’s perspective.

Target Detection and Interference Mitigation in Future AI-Based Radar Systems Hai Deng, Braham Himed

Keywords: Radar Detection, Interference Mitigation, Artificial Intelligence (AI), Inhomogeneous Clutter

A new target detection and interference mitigation approach is proposed for future artificial intelligence (AI) - based radar. Targets in AI radar systems are detected based on target and interference classification without removing interference, in contrast to interference cancelation processing in traditional radar target detection approaches. The general AI target detection approach is presented and demonstrated to be more robust and effective in target detection in unknown environments.

134 IEEE RADARCONF 2021 15 ABSTRACTS

Radar Recognition Techniques and Applications Begins: 5/12/2021 11:40 Ends: 5/12/2021 13:20 Location: Virtual Room C Chaired by Maria Sabrina Greco and Matthew Ritchie

A Multi-Radar Architecture for Human Activity Recognition in Indoor Kitchen Environments Ali Gorji, Thomas Gielen, Marc Bauduin, Hichem Sahli, André Bourdoux

Keywords: Human Activity Recognition, Machine Learning, Radar Signal Processing, Target Tracking

This paper tackles the Human Activity Recognition (HAR) in the kitchen environment from two different angles and sensor positions. The setup includes two single radar platforms mounted on ceiling and wall of a room. The data collected from concurrently operated radars was used to evaluate the efficacy of the HAR. In addition, an indoor kitchen scenario in the presence offurniture is considered and the HAR is taken as a procedure to detect common cooking-related activities by a single human subject where a machine learning model is developed to address the HAR as a multi-class classification problem. Our experimental results show the superior performance of the proposed method in detecting kitchen activities, especially, when the features from both the radars are being fused in the central processor.

A Robust Real-Time Human Activity Recognition Method Based on Attention-Augmented GRU Qiang Jian, Shisheng Guo, Pengyun Chen, Peilun Wu, Guolong Cui

Keywords: Human Activity Recognition, Gated Recurrent Units, Ultra Wide Band Radar, Real-Time Recognition

We proposed a robust real-time human activity recognition method based on attention-augmented Gated Recurrent Unit (GRU) using radar range profile, namely Attention-Augmented Sequential Classification (AASC). We use attention mechanism to capture the temporal relationships inherent in the range profile signatures. Therefore, our model can learn long-term temporal correlation of human activity without increasing the depth or width of recurrent neural network. The attention weights are adaptively generated using features extracted by the GRU recurrent neural network. Finally, attention-augmented features are classified by Multi-layer perceptrons. Real data of picking, boxing, rasing leg and rasing hand are collected to evaluate our model. It is shown that the proposed method outperforms the conventional GRU in recognition accuracy and robustness, demonstrating the superiority in real-time activity recognition task.

Micro-Doppler Signal Decomposition Using Second-Order Vertical Synchrosqueezing Karol Abratkiewicz, Piotr Samczyński, Krzysztof Kulpa

Keywords: Short-time Fourier Transform, Vertical Synchrosqueezing, Second-Order Vertical Synchrosqueezing, micro-Doppler, Signal Decomposition

The concept of using second-order vertical synchrosqueezing (VSS) in the time-frequency (TF) domain for the decomposition of the micro-Doppler signal is described in this paper. In general, the technique aims at sharpening the energy distribution on the TF plane, but contrary to the known reassignment method, allows one to retain phase information and reconstruct the signal or a distin-

135 IEEE RADARCONF 2021 15 ABSTRACTS guished component, which favors analysis of multi-component waveforms such as micro-Doppler signatures. Theoretical considerations are briefly described and then an implementation ofthe technique and its application in order to analyze a real-life micro-Doppler signal is presented. Nu- merical validation shows that the second-order VSS and an algorithm that serves to extract signal modes can be efficiently used in signal decomposition and target description.

Graph and Projection Pursuits Approach for Time Frequency Analysis Bingcheng Li

Keywords: Graph, Projection Pursuits, Time Frequency Analysis, Pulse-On-Pulse Signal Separation, Spectrogram

In high density radio frequency (RF) signal environments, receivers may acquire multiple source signals. These multiple source RF signals may be cochannel and co-duration and cause significant difficulty for traditional time frequency approach to process them. In this paper, combining graph and projection pursuits techniques, we propose a new time frequency analysis approach for RF signal processing. The proposed approach allows us to separate cochannel and co-duration frequency modulation RF (FMRF) signals with low implementation cost.

Automatic Modulation Recognition for Overlapping Radar Signals Based on Multi-Domain SE-ResNeXt Yehan Ren, Weibo Huo, Jifang Pei, Yulin Huang, Jianyu Yang

Keywords: Automatic Modulation Recognition, Overlapping Radar Signals, Neural Networks, Multi Domain Fusion

This paper proposes a Multi-Domain SE-ResNeXt method based on multi branch feature fusion, which is used to recognize the modulation of overlapping radar signals under low SNR. This method combines the signal in time domain, frequency domain, autocorrelation domain and time-frequency domain, and extract high-dimensional features by SE-ResNeXt respectively. Then, fuse these features based on a SE block to model the correlation between these features automatically. The simulation results show that the recognition accuracy of the proposed method is more than 93% at -4dB and close to 100% when the SNR is more than 0dB.

Special Session: Biomedical and e-Healthcare Applications of Radar Begins: 5/12/2021 11:40 Ends: 5/12/2021 13:20 Location: Virtual Room D Chaired by Kevin Chetty and Mohammud Bocus

Continuous Human Activity Recognition for Arbitrary Directions with Distributed Radars Ronny Gerhard Guendel, Matteo Unterhorst, Ennio Gambi, Francesco Fioranelli, Alexander Yarovoy

Keywords: Micro-Doppler Classification, Distributed Radar, Machine Learning, Assisted Living, Human Activity Recognition

Continuous Activities of Daily Living recognition in an arbitrary movement direction using five distributed pulsed UWB radars in a coordinated network is proposed. Classification approaches in unconstrained activity trajectories that render a more natural occurrence for Human Activity Recog- nition are investigated. Feature and decision fusion methods are applied to the priorly extracted

136 IEEE RADARCONF 2021 15 ABSTRACTS handcrafted features. The softmax classifier provides explicit probabilities associated with each target label. The outputs of these classifiers from different radar nodes were combined witha probability prediction balancing approach over time to improve performances. The final results show average improvements between 6.8% and 17.5% compared to the usage of any single radar unconstrained directions. Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring Chong Tang, Shelly Vishwakarma, Wenda Li, Raviraj Adve, Simon Julier, Kevin Chetty

Keywords: Passive WiFi Sensing, micro-Dopplers, Activity Recognition, Deep Learning, Simulator

This work presents a simulation framework for generating micro-Doppler spectrogram for data augmentation to improve the classification accuracy for activity recognition by passive WiFi radar (PWR). It is expected to solve the problem of low classification accuracy due to the insufficient training set. SimHumalator is introduced for effective simulation data acquisition based on motion point-cloud data. The quality of the simulated data is verified by comparing across the simulation spectrogram, measurement spectrogram and the ground-truth velocity. Afterwards, we benchmark the classification performance base on two augmentation schemes: Replacement and Augmen- tation via a six-activities dataset. Experimental results show that both schemes can improve the classification accuracies by up to 8% compared to when only training the measurement data.

Interference Motion Removal for Doppler Radar Vital Sign Detection Using Variational Encoder-Decoder Neural Network Mikolaj Czerkawski, Christos Ilioudis, Carmine Clemente, Craig Michie, Ivan Andonovic, Christos Tachtatzis

Keywords: Doppler Radar, Heart Rate Monitoring, Respiration Rate Monitoring, Vital Signs, Random Body Movement, Variational Autoencoder

A novel approach to the removal of interference motions through the use of a variational encoder- decoder convolutional neural network is presented for Doppler radar vital sign detection. The approach is evaluated on semi-experimental data containing real vital sign signatures and simulated returns from interfering body motions. It is further demonstrated that the model can enhance the extraction of the micro-Doppler frequency corresponding to the respiration rate.

Physics-Aware Design of Multi-Branch GAN for Human RF Micro-Doppler Signature Synthesis Mohammad Mahbubur Rahman, Sevgi Zubeyde Gurbuz, Moeness G. Amin

Keywords: micro-Doppler, Radar, Generative Adversarial Networks, Physics-Aware Machine Learning, Gait Analysis

Generative adversarial networks (GANs) have been recently proposed for the synthesis of RF micro- Doppler signatures to mitigate the problem of low sample support and enable the training of deeper neural networks (DNNs) for improved RF signal classification. However, when applied to human micro-Doppler signatures for gait analysis, GANs suffer from systemic kinematic discrepancies that degrade performance. As a solution to this problem, this paper proposes the design of a physics-aware loss function and multi-branch GAN architecture. Our results show that RF gait signatures synthesized using the proposed approached have greater correlation and similarity to

137 IEEE RADARCONF 2021 15 ABSTRACTS measured RF gait signatures, while also improving the accuracy in classifying five different gaits.

Application of MM-Wave Radar and Machine Learning for Post-Stroke Upper Extremity Motor Assessment Edward Benavidez, Guy DeMartinis, Yining Wu, Andrew Gatesman

Keywords: Millimeter-wave Radar, Machine Learning, SVM, kNN, Fugl-Meyer Assessment, 2DPCA, Hankel Transform

This research effort summarizes the results of a pilot study in which 14 healthy participants performed a subset of the Upper Extremity Fugl-Meyer Assessment protocol used for post-stroke recovery evaluation. Radar measurements were acquired using a commercially available Texas Instruments millimeter-wave radar sensor. Machine Learning algorithms were utilized for task classification and task score prediction.

Array Processing Begins: 5/12/2021 14:20 Ends: 5/12/2021 16:20 Location: Virtual Room A Chaired by Elias Aboutanios and Braham Himed

DOA Estimation with Subarrays via Blind Source Separation Algorithm Zhengxin Yan, Mengmeng Ge, Guolong Cui, Xianxiang Yu, Rujun Hu

Keywords: Subarrays, Sum-difference Channels, BSS, DOA Estimation

In this paper, the direction of arrival (DOA) estimation in terms of weak target is addressed under strong mainlobe jamming. Specifically, a sum-difference beam signal model for multiple subarrays is introduced accounting for target echo and mainlobe jamming. For each subarray, the target and jamming signals are separated from the received sum-difference channel signals via blind source separation (BSS) algorithm. Further, monopulse technique is exploited to estimate DOA of weak target by jointing all the separated target signals of each subarray. Finally, the effectiveness and performance of the proposed technique are analyzed via numerical simulations.

Doubly-Toeplitz-Based Interpolation for Joint DoA-Range Estimation Using Coprime FDA Ruisong Cao, Shengheng Liu, Zihuan Mao, Yongming Huang

Keywords: Toeplitz Matrix, Sparse Array, Frequency Diverse Array, Interpolation, Parameter Estimation

The concept of coprime frequency diverse array (FDA) has attracted much attention in the recent years. For it reaps the benefits of coprime sampling and frequency diversity, improved degrees- of-freedom (DoF) and spatial resolution can be achieved. However, existing works adopt on-grid methods in the joint direction-of-arrival (DoA)-range estimation and, thus, a compromise between the off-grid error and the computational cost is needed. In this paper, a doubly-Toeplitz-based estimation algorithm is proposed to achieve the coarray interpolation and off-grid estimation for coprime FDA. A positive semi-definite optimization problem is formulated based on atomic-norm minimization, and the joint DoA-range estimation can be achieved by applying well-established subspace-based spectral estimation algorithm to the optimized covariance matrix. The effectiveness of proposed method is validated by numerical simulations.

138 IEEE RADARCONF 2021 15 ABSTRACTS

Range-Dependent Beamforming Using Space-Frequency Virtual Difference Coarray Tianheng Ni, Shengheng Liu, Zihuan Mao, Yongming Huang

Keywords: Sparse Array, Frequency Diverse Array (FDA), Covariance Matrix, Beamforming, Direction Of Arrival (DoA)

Frequency diverse array (FDA) combines frequency and spatial diversities and, thereby, generates a time-range-angle-dependent beampattern, which is attractive in many application scenarios. In- corporating coprimality in designing the array geometry and the frequency offset yields coprime FDA, whose spatial resolution and degrees of freedom are enhanced. In this paper, we further leverage the second-order statistics to construct the location and frequency of the coprime difference sets of coprime FDA in the virtual domain. The proposed scheme can obtain a decoupled receive beampattern with an enlarged array aperture and a reduced radio-frequency circuit complexity. The advantages of the proposed method is confirmed by the simulation results.

Radar Antenna Selection for Direction-of-Arrival Estimations Arda Atalik, Mustafa Yilmaz, Orhan Arikan

Keywords: Antenna Selection, Cognitive Radar, Direction-Of-Arrival Estimations

The number of elements utilized by a multi-antenna radar enhances the detection capabilities of the radar. However, utilizing a large number of elements may be prohibitively costly in terms of operation. Therefore, the feasibility of electronically refining antenna arrays to reduce cost of operation with only marginal loss of performance has recently attracted significant attention. Under cognitive radar paradigm, the goal is to choose an optimal or near optimal subset of elements from an antenna array of pre-specified geometry while meeting certain performance and cost criteria. In this work, we present optimization based selection methods for certain array geometries to select the best K-element sub-array in terms of Cramer-Rao lower bound on direction-of-arrival estimations. Constant Beamwidth Receiving Beamforming Based on Template Matching Ruitao Liu, Guolong Cui, Qinghui Lu, Xianxiang Yu, Lifang Feng, Jinghui Zhu

Keywords: Frequency Invariant, Steering Invariant, Template Matching, Array Gain, CCD

Aiming at the problem of frequency invariant beamforming (FIB) and steering invariant beamforming (SIB), a general constant beamwidth receiving beamforming method based on template matching is proposed. Firstly, within the constraints of array gain, the optimization model is constructed by minimizing the matching error between the template and the optimized beampattern. Then, by using cyclic coordinate descent (CCD) algorithm, the model is transformed into multiple one- dimensional subproblems with closed-form solutions, which ensures that the template matching error gradually reduces to convergence. Finally, simulation results show that the proposed method can synthesize constant beamwidth beampattern with the low sidelobe and nulling.

Virtual Array-Based Super-Resolution for Mechanical Scanning Radar Linfeng Qiu, Yongchao Zhang, Yin Zhang, Yulin Huang, Jianyu Yang

Keywords: Scanning Radar, Virtual Array, Multiple Signal Classification

139 IEEE RADARCONF 2021 15 ABSTRACTS

Source location with real aperture radar (RAR) has raised many concerns in the applications of monitoring for the interested sources. However, the coarse angular resolution of RAR is insufficient for resolving multiple sources within the beam width. To solve this problem, thispaper proposes a virtual array-based superresolution method for mechanical scanning antenna. First, the virtual array model is constructed using the azimuth echo of mechanical scanning radar, and derived the mapping relationship between the real aperture scanning radar and the virtual array. Then, the sources distribution is recovered via the virtual array model combined with multiple signal classification (MUSIC) algorithm. Last, simulation validates that the method can improve the angular resolution of mechanical scanning radar.

Cognitive Radar & Machine Learning Begins: 5/12/2021 14:20 Ends: 5/12/2021 16:20 Location: Virtual Room B Chaired by Kristine Bell and Nathan Goodman

Detecting Potential Performance Improvements in Cognitive Radar Systems Austin Egbert, Adam Goad, Charles Baylis, Robert J Marks II, Anthony Martone

Keywords: Change Detection Algorithms, Circuit Optimization, Cognitive Radar, Gradient Methods, Power Amplifiers, Radio Spectrum Management

As a cognitive radar system adapts to interference in real time by adjusting its transmission characteristics, the optimal load impedance for the transmit power amplifier will also change. A adaptive impedance tuner can be used to optimize the amplifier for improved transmit power and resultant radar range. However, the cognitive radar will continue to adapt to new interference, affecting the optimal impedance and potentially requiring reoptimization. To avoid the performance cost of un- necessary optimization, this work demonstrates use of the earth mover distance applied to utilized transmit frequencies over time to determine when reoptimization may provide meaningful output power improvement.

Constrained Online Learning to Mitigate Distortion Effects in Pulse-Agile Cognitive Radar Charles Thornton, Michael Buehrer, Anthony Martone

Keywords: Cognitive Radar, Online Learning, Radar Signal Processing, Target Detection, Spectrum Sharing

Cognitive radar is an emerging paradigm which uses feedback between the transmitter and receiver to improve performance. In this paper, we develop an online learning framework, where the radar learns to mitigate interference from non-cooperative and malicious sources while selecting from a constrained set of waveforms that reduces the appearance of ghost targets in the coher- ently processed data matrix.

140 IEEE RADARCONF 2021 15 ABSTRACTS

The Value of Memory: Markov Chain Versus Long Short-Term Memory for Electronic Intelligence Sabine Apfeld, Alexander Charlish, Gerd Ascheid

Keywords: Electronic Intelligence, Machine Learning, Recurrent Neural Network, Markov Chain, Emission Prediction, Radar Emitter Identification

In this paper, we compare Markov chains and Long Short-Term Memory neural networks for the prediction of radar emissions and the identification of the radarś type. This comparison isespe- cially interesting since Markov chains and Long Short-Term Memory networks are in contrast to each other in terms of memory. The two methods for prediction and identification are based on an emission model that understands the radarś emissions as a language with an inherent hierarchical structure. The evaluation is performed with the data of a simulated airborne multifunction radar that can make use of three resource management methods of varying complexity. It is shown that the Markov chain outperforms the Long Short-Term Memory in simple scenarios, while the neural network is better suited for more complex tasks. Moreover, its identification performance is much more robust with respect to corrupted data.

Spectral Gap Extrapolation and Radio Frequency Interference Suppression Using 1D UNets Arun Nair, Akshay Rangamani, Lam Nguyen, Muyinatu Bell, Trac Tran

Keywords: Spectral Gap Extrapolation, Radio Frequency Interference Suppression, Ultra-Wideband Radar, Convolutional Neural Network

Modern ultra-wideband (UWB) radar systems transmit a wide range of frequencies, spanning hundreds of MHz to a few GHz, to achieve improved penetration depth and narrower pulse width. A common challenge faced is the presence of other commercial transmission equipment operating in the same band, causing radio frequency interference (RFI). To overcome this RFI issue, radar systems have been developed to either avoid operating in bands with RFI, or to suppress the RFI affected signals after reception. In this work, we examine both families of operation, and demonstrate that 1D convolutional neural networks based on the UNet architecture can provide powerful signal enhancement capabilities on raw UWB radar data. The model is trained purely on simulated data and translated to real UWB data, achieving impressive results compared to traditional sparse-recovery baseline algorithms.

Quick Black Box Variational Inference Using Gaussian Cubature Integration Rules Michał Meller

Keywords: Machine Learning, Black Box Variational Inference, Bayesian Methods, Numerical Integration, Gaussian Cubature

A novel variant of black box variational inference, which employs Gaussian cubature integration rules, is proposed. The method is applicable to small and medium scale problems and is particularly well fitted for real-time applications such as radar. The application of the cubature rule results in noise-free estimates of the evidence lower bound and its gradient. This feature allows one to employ quasi-Newton optimization methods, which converge considerably faster than stochastic gradient methods used in classical black box variational inference. The improvement in convergence speed is demonstrated using the direction of arrival estimation as an example.

141 IEEE RADARCONF 2021 15 ABSTRACTS

Error Correction Output Code-Based Radar Platform Motion Type Classification Emirhan Ozmen, Fuat Cogun, Yasar Kemal Alp, Fatih Altiparmak

Keywords: Electronic Warfare, Multitask Learning, Support Vector Machine, Machine Learning, Error-Correcting Output Codes, Multiclass Classification

In this paper, radar parameters measured by EW systems are aimed to determine the platform types with which they are related. First, an efficient preprocessing method is applied, which involves quantifying range values and grouping class values to improve classification performance. Then, Multitask Learning (MTL) neural network, Support Vector Machine (SVM) and Error-Correcting Output Codes (ECOC) techniques are applied to the problem. It is observed that ECOC technique outperforms SVM and MTL technique. It is considered that the result of the classification process can be used in the selection of the type and parameters of the filter used for tracking.

Target Localization and Classification at short ranges Begins: 5/12/2021 14:20 Ends: 5/12/2021 16:20 Location: Virtual Room C Chaired by Laura Anitori and Willie Nel

Investigation of Uncertainty of Deep Learning-Based Object Classification on Radar Spectra Kanil Patel, William Beluch, Kilian Rambach, Adriana-Eliza Cozma, Michael Pfeiffer, Bin Yang

Keywords: Uncertainty, Deep Learning, Object Classification, Automotive Radar, Overconfidence

Deep learning-based Radar object classification has shown great initial success. Though inad- dition to highly accurate predictions, it is crucial that these networks also provide reliable confi- dences. However, we show that, instead, they are overly confident, even in their wrong predictions. In particular, we evaluate how uncertainty quantification can support radar perception under domain shift, corruptions of input signals, and in the presence of unknown objects. As a first solution, we propose applying post-hoc uncertainty calibration to improve the quality of the confidence measures and show that to a large extent this over-confidence of the radar networks can be mitigated.

DEEPREFLECS: Deep Learning for Automotive Object Classification with Radar Reflections Michael Ulrich, Claudius Gläser, Fabian Timm

Keywords: Radar, Deep Learning, Automotive, Embedded, Classification

This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The method provides object class information suchas pedestrian, cyclist, car, or non-obstacle. The method is both powerful and efficient, by using a light-weight deep learning approach on reflection level radar data. It fills the gap between low- performant methods of handcrafted features and high-performant methods with convolutional neural networks. The proposed network exploits the specific characteristics of radar reflection data: It handles unordered lists of arbitrary length as input and it combines both extraction of local and global features. In experiments with real data the proposed network outperforms existing methods of handcrafted or learned features. An ablation study analyzes the impact of the proposed global context layer.

142 IEEE RADARCONF 2021 15 ABSTRACTS

Comparison of Different Approaches for Identification of Radar Ghost Detections in Automotive Scenarios Yi Jin, Robert Prophet, Anastasios Deligiannis, Ingo Weber, Juan-Carlos Fuentes-Michel, Martin Vossiek

Keywords: Automotive Radar, 77GHz, Machine Learning, Deep Learning, Classification, Ghost Detection

This paper uses 3 data-based approaches, namely random forest, convolutional neural network, and PointNet++, to classify the radar ghost detection in the city and motorway scenarios. For each approach, the influence of various features is also studied and analyzed. Random forestand PointNet++, with more than 95% accuracy, outperform the convolutional neural network.

Moving Target Classification Based on Micro-Doppler Signatures via Deep Learning Yonatan David Dadon, Shahaf Yamin, Stefan Feintuch, Haim Henry Permuter, Igal Bilik, Joseph Taberkian

Keywords: Moving Target Classification, AI, Deep Learning, CNN, Data Augmentation For Radar Data, Micro-Doppler Effect, Spectogram, Regularization

Radar-based classification of ground moving targets relies on Doppler information. Therefore, the classification between humans and animals is a challenging task due to their similar Dopplersig- natures. This work proposes a Deep Learning-based approach for ground moving radar targets classification. The proposed algorithm learns the radar targetsḿicro-Doppler signatures inthe 2D fast-time slow-time domain of radar echoes. This work shows that the convolutional neural network (CNN) can achieve high classification performance. Also, it shows that efficient dataaug- mentation and regularization significantly improve classification performance and reduce over-fit.

WALDO Finds You Using Machine Learning: Wireless Adaptive Location and Detection of Objects Aditya Singh, Pratyush Kumar, Vedansh Priyadarshi, Yash More, Aishwarya Das, Debayan Gupta

Keywords: Software-Defined Radio (SDR), Short-time Fourier Transform (STFT), Low Frequency RF-waves

We present a novel radar-based system for real-time indoor positioning and detection of objects and human-bodies with low-quality, inexpensive sensors. Using modern deep learning methods to classify and localise objects and materials in real-time, we are able to obviate both expensive hardware parts of the traditional signal processing chain. Crucially, our system operates in the less crowded low-frequency range of 433 MHz in contrast to existing RF-based sensing methods, allowing us to use cheap, off-the-shelf hardware. Based on established research designs, we report high-accuracy results on: (1) classification of different objects/materials (plastic, glass, metal); (2) detection and classification of multiple visually and materially similar objects and the human-body; and (3) simultaneous location of multiple objects in a two-dimensional grid of size 2.1m*1.74m.

Deep Transfer Learning for WiFi Localization Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham

Keywords: WiFi, Indoor Localisation, Deep CNN, Transfer Learning

143 IEEE RADARCONF 2021 15 ABSTRACTS

This paper studies a WiFi indoor localisation technique based on using a deep learning model and its transfer strategies. We take CSI packets collected via the WiFi standard channel sounding as the training dataset and verify the CNN model on the subsets collected in three experimental environments. We achieve a localisation accuracy of 46.55 cm in an ideal (6.5m*2.5m) office with no obstacles, 58.30 cm in an office with obstacles, and 102.8 cm in a sports hall (40*35m). Then, we evaluate the transferability of the proposed model to different environments. The experimental results show that, for a trained localisation model, feature extraction layers can be directly transferred to other models and only the fully connected layers need to be retrained to achieve the same baseline accuracy with non-transferred base models. This can save 60% of the training parameters and reduce the training time by more than half. Finally, an ablation study of the training dataset shows that, in both office and sport hall scenarios, after reusing the feature extraction layers ofthe base model, only 55% of the training data is required to obtain the modelsáccuracy similar to the base models.

Software Defined Radar & Low-cost radar Begins: 5/12/2021 14:20 Ends: 5/12/2021 16:20 Location: Virtual Room D Chaired by Piotr Samczynski and John Stralka

Reverse Engineering the Soli Radar API for Military Applications Khaled Basrawi, Richard Dill

Keywords: Android, Dynamic Hooking, Reverse Engineering, Soli Radar

Through-wall radar systems are widely adopted for first responder safety and military applications. Current solutions are expensive and bulky. We investigate the Google Soli radar, designed for fine gesture detection to facilitate human-computer interaction, as an alternative solution. Google has not publicly released the radarś API, which is necessary to exploit the radar. We reverse-engineered the packages and classes that receive information from the radar before passing it to consumer application packages within the Google Pixel 4. We extracted information and demonstrated cap- turing the Soli radar output via dynamic hooking, and demonstrated its performance in detecting motion through walls.

An FPGA Based 24 GHz Radar Testbed for Physical-Layer Cyberattack Research Onur Toker

Keywords: FPGA, Radar Testbed, Physical-layer Cyberattacks

In this paper, we present a low cost Ka-band radar testbed using commercial off-the-shelf components. The motivation for this design comes from the popular S-band radar known as the MIT radar or coffee-can radar. To be able to do experimental research and real-time tests with a radar, we need two critical subsystems (1) A microwave circuit with I/Q outputs, and (2) A baseband processing system with high-speed ADC/DACs. Our main objective for building this testbed is to use it for physical-layer cyberattack research, attack resilience testing, OFDM radar and joint radar communication tests. The proposed microwave circuit is built using commercial off-the-shelf microwave parts, and the baseband system is another commericial off-the-shelf FPGA based Zynq SOC with 100 MSa/s dual ADC and dual DAC. In a future version of this paper, more detailed examples with

144 IEEE RADARCONF 2021 15 ABSTRACTS

FPGA design and source codes will be presented.

Compressive Sensing Based Software Defined GPR for Subsurface Imaging Yan Zhang, Daniel Orfeo, Dryver Huston, Tian Xia

Keywords: Software Defined Radar, Subsurface Imaging, Compressive Sensing, Structural Similarity Index Measure

This paper presents a new ground software defined ground penetrating radar (SD-GPR) integrating compressive sensing (CS). In the operation, the SD-GPR radiates a series of sinusoidal signals at each scan position. As each individual frequency tone is transmitted and received sequentially, it results in a slow scan speed. In this study, CS is explored to expedite scan speed utilizing the scan area spatially sparsity. A CS based signal processing algorithm is then designed. To effectively reconstruct SD-GPR images, an automatic parameter selection algorithm based on the structural similarity index measure (SSIM) is proposed. For validation, a laboratory test was conducted.

Retrodirective Cross-Eye Jammer Implementation Using Software-Defined Radio (SDR) Frans-Paul Pieterse, Warren du Plessis

Keywords: Electronic Warfare (EW), Monopulse Radar Countermeasures, Tracking Radar, Software-Defined Radio (SDR)

Extremely few implementations of retrodirective cross-eye jamming have been published, and those that have either only present results without providing implementation details or are not true retrodirective cross-eye jammers. To address this limitation, the implementation and testing of a retrodirective cross-eye jammer against a phase-comparison monopulse radar are described. Both the radar and jammer were implemented using software-defined radio (SDR) and power di- viders. Calibration of the cross-eye jammer based on minimising the sumchannel radar return is described. The presented results show that the system is a true retrodirective cross-eye jammer and that cross-eye jamming can indeed induce large angular errors in monopulse radars, with conditions corresponding to a breaklock condition being achieved.

Design of a New Low-Cost Miniaturized 5.8GHz Microwave Motion Sensor Long Jin, Rui Cao, Dongsheng Li, Dandan Wang

Keywords: Doppler Effect, Microwave Motion Sensor, Non-Contact, Bridge Mixer

The designed low-cost 5.8GHz microwave motion sensor uses a new type of non-isolated branch bridge as the distribution of the transmit signal, local oscillator signal, and receive signal. The bridge is terminated with a pair of reverse configuration diodes to complete balanced mixing. The antenna of the sensor is a shared microstrip patch antenna for receiving and sending. The local oscillator signal and the transmission signal come from the same oscillator. Except for the non- isolated bridge, the distribution of the local oscillator and the transmission does not require an additional power distribution network. The antenna port and oscillator port of the bridge are non- isolated ports, and the two ports connected to the diode are isolated. The new electric bridge has good symmetry and maintains the balance of the traditional electric bridge mixer. The volume of the microwave motion sensor using this new type of bridge mixer is only 18mm*20mm*1.5mm.

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Architecture Study for a Bare-Metal Direct Conversion Radar FPGA Testbench Randall Summers, Mark Yeary, Hjalti Sigmarsson, Rafael Rincon

Keywords: DSP, FPGA, Data Conversion

This paperś technical contribution is a system architecture for a high-bandwidth radar testbench. Our results provide the resource utilization and floorplanning for a Virtex 7 FPGA. The term testbenches refers to pieces of code used during FPGA or ASIC simulation that employ specific clocks, interfacing to other devices, data input/output, etc. to validate an FPGA design. As high-bandwidth all-digital radar platforms become increasingly prevalent, new signal processing techniques and testbenches need to be explored to handle real-time processing of high-bandwidth data streams. Herein, a modular radar signal processing testbench architecture is discussed, along with some key implementation details and challenges.

THURSDAY

Special Session: Spaceborne SAR Missions: State of the Art and Future Developments Begins: 5/13/2021 8:00 Ends: 5/13/2021 9:40 Location: Virtual Room A Chaired by Dirk Geudtner and Alberto Moreira

Copernicus and ESA SAR Missions Dirk Geudtner, Nico Gebert, Michel Tossaint, Malcolm Davidson, Florence Heliere, Ignacio Navas Traver, Robert Furnell, Ramon Torres

Keywords: SAR, InSAR, Sentinel-1, ROSE-L, Sentinel-1 Next Generation, BIOMASS

This paper provides an overview of the Copernicus SAR missions, namely Sentinel-1, ROSE-L and Sentinel-1 Next Generation (NG). In particular, we discuss the Sentinel-1 SAR and interferometry performance. Further, we describe the key characteristics of the ROSE-L SAR instrument. For the Sentinel-1 NG mission, we discuss potential SAR performance enhancements and novel imaging capabilities. In addition, we present different SAR system concepts and results of the preliminary mission analysis, addressing coverage and revisit time. Finally, we discuss the ESA Earth Explorer BIOMASS P-band SAR mission and its SAR instrument. NASA-ISRO SAR (NISAR) Mission Status Paul Rosen, Raj Kumar

Keywords: Synthetic Aperture Radar, Repeat Pass Radar Interferometry, Radar Polarimetry, Earth Science Missions

The National Aeronautics and Space Administration (NASA) in the United States and the Indian Space Research Organisation (ISRO) are developing the NASA-ISRO Synthetic Aperture Radar (NISAR) mission. The mission will exploit synthetic aperture radar to map Earth’s surface every 12 days, persistently on ascending and descending portions of the orbit, over all land and ice-covered surfaces. This single observatory solution with an L-band (24 cm wavelength) and S-band (10 cm wavelength) radar has a swath of over 240 km at fine resolution, using full polarimetry where

146 IEEE RADARCONF 2021 15 ABSTRACTS needed. To achieve these unprecedented capabilities, both radars use a reflector-feed system, whereby the feed aperture elements are individually sampled to allow a scan-on-receive (“Sweep- SAR”) capability at both L-band and S-band. The L-band and S-band electronics and feed apertures, provided by NASA and ISRO respectively, share a common 12-m diameter deployable reflector/- boom system, provided by NASA. This paper gives a status update on the mission development and changes to the observatory functions and operations.

German Spaceborne SAR Missions Alberto Moreira, Manfred Zink, Michael Bartusch, Elizabeth Nuncio Quiroz, Samuel Stettner

Keywords: Spaceborne SAR, Bistatic SAR, Interferometry, Digital Beamforming, High-Resolution Wide-Swath (HRWS) SAR

This paper provides an overview of the German spaceborne radar program since the 90s with the participation in the Shuttle Imaging Radar missions with an X-band radar instrument. The national satellite radar program began in 2007 with the launch of the satellite TerraSAR-X which is providing since then high-resolution X-band images for scientific and commercial applications. TanDEM- X, an almost identical twin of TerraSAR-X, was launched in 2010. Both satellites form the first bistatic spaceborne SAR system consisting of two satellites in close formation flight. A global, high-resolution digital elevation model of the Earth surface with unprecedented accuracy has been generated and made available for a broad community since 2016. The paper concludes with an overview of the innovative concepts, technologies, imaging techniques and applications planned for the future national spaceborne SAR missions Tandem-L and HRWS.

Overview of ALOS-2 and ALOS-4 L-Band SAR Takeshi Motohka, Yukihiro Kankaku, Satoko Miura, Shinichi Suzuki

Keywords: Synthetic Aperture Radar (SAR), Satellite, ALOS, Active Phased Array Antenna, Digital Beam Forming (DBF)

ALOS-2 and ALOS-4 are Japanese earth observation satellites with L-band SAR sensors. ALOS-2 has been operating since 2014, and its observations have been utilized for disaster mitigation, environmental monitoring, and technology development. ALOS-4 is now being developed for launch in JFY2022. The ALOS-4 aims to expand swath width and increasing observation frequency while keeping the high spatial resolution of ALOS-2 for improving the response of disaster monitoring, early detection of anomalies on the earthś surface, and enabling time-series analysis. In this paper, the ALOS-2/4 mission summary and SAR system design are introduced.

RADARSAT Constellation Mission Overview and Status Guennadi Kroupnik, Daniel De Lisle, Stephane Côté, Mélanie Lapointe, Catherine Casgrain, Réjean Fortier

Keywords: SAR Satellite, RADARSAT, Constellation, RCM, Compact Polarimetry

Successor to RADARSAT-1(1995) and RADARSAT-2(2007), the RADARSAT Constellation, launched in June 2019, is Canada’s next spaceborne radar mission consisting of three identical SAR satellites flying in a constellation which provides complete coverage of Canadaś land and oceans,of- fering an average daily revisit, as well as a potential daily access to 95% of any location on the globe. At time of writing, commissioning of the mission is proceeding well, with a plan to move

147 IEEE RADARCONF 2021 15 ABSTRACTS into early operations over the next weeks.

Multichannel and Multistatic Passive Radar Begins: 5/13/2021 8:00 Ends: 5/13/2021 9:40 Location: Virtual Room B Chaired by Pierfrancesco Lombardo and Daniel O’Hagan

Complementary Direct Data Domain STAP for Multichannel Airborne Passive Radar Diego Cristallini, Luke Rosenberg, Philipp Wojaczek

Keywords: Passive Radar, Direct Data Domain, DVB-T, STAP, Airborne, Moving Platform

In recent years, passive radar has been investigated for airborne applications in imaging and detection of ground targets. There are challenges with detection of land targets from an airborne platform due to platform motion which causes the clutter Doppler to spread and the presence of discrete scatterers which can be strong and confused with targets of interest. In this paper, the problem of heterogeneity in the ground clutter is addressed through a complementary direct data domain algorithm that exploits knowledge of the ground clutter. Once an estimate of the ground clutter is found, it can be subtracted from the original range Doppler map to reveal potential targets. The performance of this novel multichannel algorithm is assessed using a Monte Carlo simulation with simulated data. Performance Analysis of LTE Signals in RD-STAP Applications Sureshan Suntharalingam, James Lievsay

Keywords: STAP, Passive Radar, GMTI

Widespread availability of long-term evolution (LTE) signals makes them potential for use in PBR applications. However, their utility in such applications is not yet fully explored. This research focuses on the key LTE signal attributes, such as modulation schemes and bandwidth, and their effect on PBR application. Space-time adaptive processing (STAP) concepts, both full-dimension (FD-STAP) and reduced-dimension (RD-STAP), were employed to evaluate and compare the effects of varying these signal attributes, in terms of signal to interference-plus-noise ratio (SINR) metrics.

An Adaptive Fusion Algorithm for Multistatic and Multichannel Passive Radar Detection Junkang Wei, Junjie Li, Chunyi Song, Zhiwei Xu, Kai Ding

Keywords: DVB-S, Multistatic Multichannel Passive Bistatic Radar, Weight Fusion

Passive bistatic radar is of great interest in both civilian and military applications due to its numerous advantages. However, it also faces a great challenge of detecting weak echo signals. In this paper, a multistatic and multichannel passive radar detection system is constructed, which uses digital video broadcast-satellite as illuminator of opportunity. Furthermore, an adaptive fusion combination framework for moving target detection is then proposed, which exploits the optimization criterion of the modified deflection coefficient to optimize the weight that is designed fortest statistics obtained in each individual static, and then yields the global decision through weighted fusion of the all detection statistics. Simulation results illustrate that the proposed algorithm outperforms traditional ones under time-varying interference.

148 IEEE RADARCONF 2021 15 ABSTRACTS

First Experimental Results on Multi-Angle DVB-S Based Passive ISAR Exploiting Multipolar Data

Fabrizio Santi, Iole Pisciottano, Debora Pastina, Diego Cristallini

Keywords: Passive Radar, Passive ISAR, DVB-S Based Passive ISAR, Passive Polarimetry, Multidimensional Imaging

This work investigates the potentialities of a passive multidimensional ISAR imaging based on DVB-S transmitters of opportunity. Image fusion methods exploiting diversities in both the spatial and polarimetric domains are introduced, aiming at achieving an image with better quality than the images acquired by the individual observation angles and/or polarimetric channels. An analysis using experimental datasets comprising a passive receiver able to collect signals in both the horizontal and vertical polarizations, a cooperative turning ferry, and an Astra satellite has been provided.

Passive Multistatic Radar Imaging with Prior Information Airas Akhtar, Bariscan Yonel, Birsen Yazici

Keywords: Algorithm With Sparsity Prior, Multistatic Radar, Generalized Wirtinger Flow (GWF)

Our work extends the framework of Generalized Wirtinger Flow (GWF) [19] to incorporate prior information regarding the signal of interest (which is scene reflectively in our case). For interferometric multi-static radar measurements, we consider a linear-deterministic-discrete lifted forward model. Several structures on the signal can prove beneficial in the recovery process. In particular, we leverage the sparse structure of the unknown signal and show through numerical simulations that sparsely constrained GWF converges at a faster rate than a recovery method that does not acknowledge sparse structure.

Spectrum Sharing Begins: 5/13/2021 8:00 Ends: 5/13/2021 9:40 Location: Virtual Room C Chaired by Daniel Bliss and Aboulnasr Hassanien

Harmonic Mean SINR Maximization in a Cognitive Radar with Communication Spectrum Sharing Junhui Qian, Luca Venturino, Marco Lops, Xiaodong Wang

Keywords: Shared Spectrum Access, Radar-Communication Convergence, Non-Homogeneous Interference, Cognitive Radar

We investigate the shared spectrum access between a communication system and a radar which inspects multiple range gates with non-homogeneous interference. The degrees of freedom for system design are the radar probing signal, the radar receive filters at each range gate, and the communication codebook. Assuming some cognition of the environment, we propose to maximize the harmonic mean of the signal-to-interference-plus-noise ratio across the range gates inspected by the radar under semi-definite constraints on the communication mutual information, the transmitted power, and the similarity of the radar code to a desired reference signal. Numerical examples are provided to assess the merits of the proposed solution.

149 IEEE RADARCONF 2021 15 ABSTRACTS

Memory NLEQ Techniques to Mitigate Cross-Modulation Effects in Radar Euan Ward, Bernard Mulgrew

Keywords: Radar, Cross-modulation, Nonlinearity, Volterra Series, NLEQ, DPD, Nonlinear Memory, Receiver, Nonlinear Equalization, Digital Post-Distortion

This paper presents a study into the effectiveness of the NLEQ technique in mitigating cross- modulation effects in radar. Importantly, the study extends the NLEQ technique to include nonlinear memory effects for the first time in the radar literature and presents detailed PD analysis on its effectiveness in the cross-modulation scenario. As a result, the NLEQ inverse must include memory effects if it is to be successful in correcting for cross-modulation generated from a forward receiver nonlinearity with memory. Additionally, it’s shown that complex NLEQ memory inverse structures that can compensate for nonlinear memory effects in radar can be successfully identified. Detection Performance of Embedded QPSK Onto LFM Waveform Guard Bands for RF Convergence Jann Rohde, Ric Romero

Keywords: QPSK, LFM, RF Convergence, Spectrum Sharing, Waveform Design, Waterfilling, Guard Bands, Spectral Efficiency, Estimation, SER, Pd

In this work, we investigate the use of the guard bands of a linear frequency modulated (LFM) radar waveform for communications. For illustration, quaternary phase-shift keying (QPSK) is used. The effect of the LFM waveform on the symbol error rate (SER) is investigated. Conversely, the effect of injecting the QPSK RF carriers on the radar’s probability of detection (PD) is also investigated. To reduce the interference on each other, a spectral distance metric is introduced and its effect on SER and PD is evaluated. To mitigate the obvious effect or large radar power compared to communications, radar mainlobe notch filtering, parameter estimation and subtraction are used in the communications receiver. In terms of the radar’s PD, the effect of low-power communications due to the use of the guard bands turns out to be minimal.

Study of OAM for Communication and Radar Daniel Orfeo, Dryver Huston, Tian Xia

Keywords: Orbital Angular Momentum, Radar, Communication

Dual function radio-frequency sensing and communication systems offer the promise of improved spectral efficiency and streamlined hardware requirements. Control of orbital angular momentum (OAM) may be used to increase data-rates and improve radar sensitivity to certain chiral targets. This paper presents finite-difference time-domain simulations which model a gigahertz-frequency OAM radar capable of transmitting information via OAM-mode modulation. The unique chirality- detection capability of OAM radar is demonstrated, as well as simple information transmission. Simulation scope and radar specifications are designed with an eye toward developing a dual function ground penetrating radar (GPR) with OAM mode control.

150 IEEE RADARCONF 2021 15 ABSTRACTS

Mutual Interference Alignment for Joint Phased Array Radar and Communication Systems Bingqing Hong, Wenqin Wang, Hu Li

Keywords: Spectrum Sharing, Radar-Communication, Interference Suppression, Interference Alignment, Mutual Interference, Phase Array (PA), Multiple-Input Multip

In this paper, we propose a mutual interference steering (IA) method for interference elimination in co-existing Phased Array (PA) radar and multi-user MIMO communication systems, namely, radar- communication systems. Although ergodic interference alignment (IA) method have been proposed to cancel the mutual interferences, it will definitely reduce the degrees-of-freedom (DoFs) of communication systems. Differently, our proposed mutual IA method allows to improve the DoFs of communication system without sacrificing the radar performance. It is examined bythe Neyman-Pearson (NP) test for radar functionality, while the communication performance is evaluated by DoFs and bit error rate (BER) analysis. Both theory analysis and numerical results are provided to validate the effectiveness of the proposed IA method for co-existing radar and communication systems.

Special Session: Synergistic Radar Signal Processing and Tracking Begins: 5/13/2021 8:00 Ends: 5/13/2021 9:40 Location: Virtual Room D Chaired by Erik Leitinger and Florian Meyer

A Message Passing Based Adaptive PDA Algorithm for Robust Radio-Based Localization and Tracking Alexander Venus, Erik Leitinger, Stefan Tertinek, Klaus Witrisal

Keywords: Obstructed Line-Of-Sight, Multipath, Message Passing, Probabilistic Data Association, Belief Propagation

We present a message passing algorithm for localization and tracking in multipath-prone environments that implicitly considers obstructed line-of-sight situations. The proposed adaptive probabilistic data association algorithm infers the position of a mobile agent using multiple anchors by utilizing delay and amplitude of the multipath components (MPCs) as well as their respective uncertainties. By employing a nonuniform clutter model, we enable the algorithm to facilitate the position information contained in the MPCs to support the estimation of the agent position without exact knowledge about the environment geometry. Our algorithm adapts in an online manner to both, the time-varying signal-to-noise-ratio and line-of-sight (LOS) existence probability of each an- chor. In a numerical analysis we show that the algorithm is able to operate reliably in environments characterized by strong multipath propagation, even if a temporary obstruction of all anchors oc- curs simultaneously.

Graph-Based Multiobject Tracking with Embedded Particle Flow Wenyu Zhang, Florian Meyer

Keywords: Multiobject Tracking, Particle Flow, Factor Graphs, Sum-Product Algorithm

Seamless situational awareness provided by modern radar systems relies on effective methods for multiobject tracking (MOT). This paper presents a graph-based Bayesian method for nonlin-

151 IEEE RADARCONF 2021 15 ABSTRACTS ear and high-dimensional MOT problems that embeds particle flow. To perform operations on the graph effectively, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance with a relatively small number of particles even if object states are high dimensional and sensor measurements are very informative. Simulation results demonstrate reduced computational complexity and memory requirements as well as favorable detection and estimation accuracy in a challenging 3-D MOT scenario.

Joint Waveform and Guidance Control Optimization by Statistical Linearisation for Target Rendezvous Alessio Benavoli, Alessio Balleri, Alfonso Farina

Keywords: Cognitive Rendezvous, Adaptive Waveform Design, Cognitive Radar, Fisher Information Matrix (FIM), Cramer-Rao Lower Bound (CRLB), Linear Quadratic

The algorithm proposed in this paper jointly selects the transmitted waveform and the control input so that a radar sensor on a moving platform can prosecute a target by minimising a predefined cost that accounts for the energy of the transmitted radar signal, the energy of the platform control input and the relative position error between the platform and the target. The cost is a function of the waveform design and control input. The algorithm extends the existing Joint Waveform Guidance and Control Optimisation (JWGCO) solution to non-linear equations to account for the dependency of the radar measurement accuracies on Signal to Noise Ratio (SNR) ratio and, as a consequence, the target position. The performance of the proposed solution based on statistical linearisation is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps.

EM-Based Radar Signal Processing and Tracking Alan Nussbaum, Byron Keel, William Dale Blair, Umakishore Ramachandran

Keywords: Signal Processing, Tracking, Expectation-Maximization Algorithm, Accelerating Targets, Sensor Processing And Architectures

Maximizing the achievable SNR benefits the measurement’s precision that is affected by thermal noise, and track precision is enhanced given precise, unbiased, range measurements. While the radar is tracking the kinematic state (i.e., position, velocity, and acceleration) of the target, optimal signal processing requires knowledge of the targetś signature, range rate, and radial acceleration. Increasing the SNR of individual measurements by enhanced signal processing results in reduced settling time for the track filter and less valuable radar resources required to achieve a specified track quality. Moving targets introduce range-walk (RW), which degrades coherent processing leading to a reduction in SNR and a blurring of the point target response in range-Doppler (RD), resulting in degraded range estimates to the Tracker. In the paper, the Expectation-Maximization (EM) algorithm, an iterative and inference approach, provides improved parameter estimation and SNR by employing a Kalman filter within the Signal Processor to account for changes in thetargetś kinematics. The improved state estimates are used in the Signal Processor to achieved enhanced RW correction.

152 IEEE RADARCONF 2021 15 ABSTRACTS

Simultaneous Localization of a Receiver and Mapping of Multipath Generating Geometry in Indoor Environments Christian Gentner, Markus Ulmschneider, Rostislav Karásek, Armin Dammann

Keywords: Positioning, MPC, Multipath Assisted, Layout Estimation, Room Geometry

With Channel-SLAM we introduced an algorithm which uses multipath components (MPCs) for positioning instead of mitigating them. In this paper, we show that MPCs allow us in addition to estimating the position of a receiver, to infer valuable geometric information on the locations of reflecting surfaces and scatterers. We show how we represent this geometric information inthe form of metric maps and how these maps relate to other geometric representations such as floor plans of indoor or urban environments. To verify the proposed algorithm, we evaluate the algorithm based on measurements using an ultra-wideband (UWB) system.

Information Extraction from SAR images Begins: 5/13/2021 9:40 Ends: 5/13/2021 11:20 Location: Virtual Room A Chaired by Stefan V. Baumgartner and Birsen Yazici

CNN for Radial Velocity and Range Components Estimation of Ground Moving Targets in SAR Amir Hosein Oveis, Elisa Giusti, Selenia Ghio, Marco Martorella

Keywords: Convolutional Neural Network (CNN), Synthetic Aperture Radar (SAR), Motion Parameter Estimation, Ground Moving Target Indication

Ground-moving objects in synthetic aperture radar (SAR) images appear defocused and azimuthally displaced using conventional SAR image formation algorithms. In this paper, a novel regression method based on convolutional neural networks (CNNs) for the estimation of radial velocity and slant range components of ground moving targets is proposed. Motion parameters estimation can be helpful for designing additional matched filters to focus and relocate moving targets. Wehave generated the training and the test data in such a way that each image is indeed a 2D data matrix of a moving target. In other words, each complex image contains the range-compressed signal of only one moving target with a specified pair of (range, radial velocity). To further decrease thees- timation error, we employed transfer learning by fine-tuning the pretrained AlexNet architecture in a regression problem. To verify the effectiveness of the proposed method, simulations have been performed. The results demonstrate the effectiveness of the proposed method.

Ship Classification Based on Sidelobe Elimination of SAR Images Supervised by Visual Model Hongliang Zhu

Keywords: SAR, Ship Classification, Sidelobe, Visual Model, Convolutional Neural Network, Classification Accuracy

This paper proposes a novel method to eliminate the sidelobe of the strong scattering points in the shipś original SAR image, which utilizes the shipś visual modelś contour to extract the ship hull information. We extract the shipś visual modelś contour to merge with the original SAR image, preserving the shipś bodyś grayscale information in the optical model contour and rejecting the rest grayscale information outline. Finally, we construct a convolutional neural network with a simple

153 IEEE RADARCONF 2021 15 ABSTRACTS structure to train the newly generated SAR image to finish the ship classification task.

Self-Supervised Speckle Reduction GAN for Synthetic Aperture Radar Michael Newey, Prafull Sharma

Keywords: Synthetic Aperture Radar, Radar Clutter, Machine Learning

In this work, we present a novel generative adversarial network (GAN) for speckle reduction in synthetic aperture radar (SAR) imagery that requires only knowledge of the noise statistics. Speckle is ubiquitous in SAR and can cause problems both for human interpretability and for automated processing such as automated target recognition. The speckle reduction GAN presented in this paper does not require image pairs to train on. Instead we take the output of a smoothing CNN, subtract it from the noisy input and compared the residual noise with simulated speckle drawn using well known SAR noise characteristics. This directly encourage the smoothing CNN to remove those image features that match our simulated speckle. For smoothing and discrimination network, we utilize a simple CNN with a small number of residual network blocks, and unchanging size from layer to layer. We train and show good performance on MSTAR radar data, as well as analyzing performance on a simulated test set. We will compare our results with other methods in the literature including the self-supervised deep image prior algorithm as applied SAR data.

A Deep Deformable Residual Learning Network for SAR Image Segmentation Chenwei Wang, Jifang Pei, Xiaoyu Liu, Yulin Huang, Jianyu Yang

Keywords: SAR, Deep Learning, Image Segmentation, Convolutional Neural Network

Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Residual (SR)and CFAR detector, with the recent advancements in machine learning theory, new methods based on machine learning show better performance in SAR target segmentation. In this paper, we proposed a deep deformable residual learning network for target segmentation, which attempts to preserve the precise contour of the target. The deformable convolutional layers and residual learning block are applied in this technique, which could extract and preserve the geometric information of the target as much as possible. The proposed method is evaluated based on the Moving and Station- ary Target Acquisition and Recognition (MSTAR) dataset, and the experimental results have shown the superiority of the proposed network for the precise segmentation of the target.

Joint Image Formation and Target Classification of SAR Images Charles Connors, Theresa Scarnati, Garrett Harris

Keywords: SAR, Image Formation, Machine Learning, ATR

Recently, a significant amount of research has gone into ML techniques to target classification from SAR imagery. However, very little work has been done on using ML for SAR image formation. Classic image formation techniques solve the SAR inverse imaging problem as a disjoint step in the overall task of target classification. In this work, we propose a novel joint image reconstruction and classification technique that solves both the SAR inverse imaging problem and the target classification task within a unified ML framework. The proposed technique has the possibilityto provide performance gains to target classification networks through implementation of this fully connected network.

154 IEEE RADARCONF 2021 15 ABSTRACTS

Passive Radar Applications Begins: 5/13/2021 9:40 Ends: 5/13/2021 11:20 Location: Virtual Room B Chaired by Diego Cristallini and Krzysztof Kulpa

Passive Radar Based on LOFAR Radio Telescope for Air and Space Target Detection Mateusz Malanowski, Konrad Jędrzejewski, Jacek Misiurewicz, Krzysztof Kulpa, Artur Gromek, Mariusz Pożoga, Julia Kłos, Aleksander Droszcz

Keywords: Passive Coherent Location (PCL), Passive Radar, Radio Telescope LOFAR, Space Debris This paper presents the concept of using the low-frequency radio telescope receiver array (LOFAR) for passive detection of air and space targets using illuminators of opportunity. The LOFAR radio telescope operates in a 110-250 MHz band, thus the focus is on VHF illuminators, such as DAB (digital radio) and DVB-T (digital television). For the detection of air targets, such as aircraft, illuminators of opportunity that are relatively close to the radio telescope are considered. Due to the Earthś surface curvature, the detection of space targets, such as orbital debris, requires the use of transmitters that are far away from the receiver. The paper presents a theoretical study of the detection range for aerial and space targets and preliminary results of the detection of air targets using nearby transmitters.

UWB and WiFi Systems as Passive Opportunistic Activity Sensing Radars Mohammud Bocus, Kevin Chetty, Robert Piechocki

Keywords: UWB, WiFi, CIR, CSI, Activity Recognition, Deep/Machine Learning

We use Ultra-Wideband (UWB) and WiFi systems as passive radars for Human Activity Recognition (HAR). Five activities were performed between a transmitter and receiver, namely, sitting, standing, lying down, standing from the floor and walking. UWB Channel Impulse Response (CIR) datais fed to machine/deep learning algorithms for classifying the activities. An F1-score of 95.53% is achieved using UWB CIR data as features. Furthermore, we analysed the performance in the same physical layout using CSI data extracted from a WiFi card. Maximum F1-scores of 92.24% and 80.89% are obtained when amplitude CSI data and spectrograms are used as features, respectively.

Deinterleaving and Clustering Unknown Radar Pulses Manon Mottier, Gilles Chardon, Frédéric Pascal

Keywords: Passive Radar, Clustering, Optimal Transport

In this paper, a two-step methodology is developed to deinterlace RADAR signals. We mainly worked from a data simulator to obtain a large diversity of signals representing typical RADARs. First, a clustering algorithm is used to separate the pulses in the F-PW (Frequency-Pulse Width) plane, then a phase of cluster agglomeration is performed using hierarchical agglomerative clustering combined with optimal transport distances. Results on labeled simulated data are given.

155 IEEE RADARCONF 2021 15 ABSTRACTS

Passive Inverse Synthetic Aperture Radar Imaging from Non-Contiguous Frequency Bands Aaron Brandewie, Robert Burkholder

Keywords: Passive Radar, Inverse Synthetic Aperture Radar, Compressive Sensing, Physical Optics

Passive radar systems use signals of opportunity to illuminate targets. The signals of opportunity have lower bandwidth than dedicated active radar systems, leading to poor downrange resolution. Multiple signals of opportunity can be used to increase the overall bandwidth of the system. These signals are usually separated in the frequency domain (non-contiguous), which causes large unwanted sidelobe artifacts in the image when using a back-projection algorithm. This paper uses a compressive sensing algorithm as a method to create an inverse synthetic aperture radar (ISAR) image from the separated signals that does not contain the unwanted artifacts.

Multi-Target Delay and Doppler Estimation in Bistatic Passive Radar Systems Mohammed Rashid, Mort Naraghi-Pour

Keywords: Bistatic Passive Radar, Multitarget Estimation, Delay And Doppler Shift, Expectation Maximization

We propose a computationally efficient algorithm for localizing multiple targets on a delay-Doppler plane in a bistatic passive radar system. Our algorithm aims to compute the maximum likelihood (ML) estimate of all the targets’ delays and Doppler shifts by using the expectation maximization (EM) algorithm. The proposed method decomposes the complex multidimensional estimation for delays and Doppler shifts as well as all the targets’ parameters into several independent, per- target optimization problems. The algorithm simultaneously computes the ML estimate of all the targets’ delays and Doppler shifts, as well as the estimate of each target’s component signal in the surveillance channel (SC). The latter may be used to cancel the effect of stronger targets from the SC for localization of weaker targets. Simulation results are presented showing that the proposed algorithm outperforms the cross-correlation estimator, the modified CC (MCC) estimator and the previously proposed EM-based algorithms. In addition, in contrast to these approaches, the proposed method achieves the Cramer-Rao lower bound (CRLB).

Resource Management Begins: 5/13/2021 9:40 Ends: 5/13/2021 11:20 Location: Virtual Room C Chaired by Raviraj Adve and Alexander Charlish

Joint Jamming Beam and Power Scheduling for Suppressing Netted Radar System Dalin Zhang, Jun Sun, Wei Yi, Chengxin Yang, Yaqi Wei

Keywords: Netted Radar System, Joint Jamming Beam Selection And Power Allocation (JJBSPA), Probability Of Detection

In this paper, a JJBSPA strategy for jamming the NRS is proposed. The detection probability of netted radar under suppressive jamming is derived and utilized as a metric to evaluate jamming performance. Considering different targets have different detection performance requirements, we establish an objective function to quantify the global jamming performance. The JJBSPA optimization problem is formulated by minimizing above objective function while considering jamming

156 IEEE RADARCONF 2021 15 ABSTRACTS resource constraints to seek the optimal resource assignment result. We propose a two-step solution based on PSO as the optimization problem is non-convex. The simulation results verify the effectiveness of the proposed JJBSPA strategy.

Optimal Placement of Radars to Achieve Desired Spatially Nonuniform Probability of Detection Jase Furgerson, Dinesh Rajan

Keywords: Probability Of Detection, Binary Linear Program, Linearization, Optimization

This paper proposes an optimal way to determine the number of radar detectors and their placement to meet a desired spatially nonuniform probability of detection. We also derive a first order Taylor series approximation to linearize the joint probability of detection constraint. This linearized constraint allows the problem to be formulated as a binary linear program, which can be solved in a computationally efficient manor to support resource management of adaptive reconfigurable radar deployments.

A Reconfigurable Resource Manager for Distributed Networked Radar Reid McCargar, Graeme Smith

Keywords: Radar Resource Management, Distributed Radar, Networked Radar, Multistatic Radar, Cognitive Radar

A distributed radar network may be considered as a collection of nodes that can be combined into several “sub-apertureś́ such that each sub-aperture is optimized for a particular task the network must complete. This approach allows matching of radar node resources to tasks and thus maximization of the number of tasks that can be undertaken in parallel. If the sub-apertures are defined dynamically we might consider the radar network as being continuously reconfigured. This leads to the concept of reconfigurable resource management (RRM) which is a new form of resource management (RM) where the sensor can be reconfigured into multiple sub-sensors to maximize resource use. A general mathematical framework is presented for RRM. A Monte Carlo simulation is presented, in which a network of two gimbaled dish radars must maintain custody of multiple target tracks. The RRM approach is found to significantly outperform static RM (SRM): when there are twenty-five tasks SRM has a success rate of just 40% compared to RRMś100%.

Time Budget Management in Multifunction Radars Using Reinforcement Learning Petteri Pulkkinen, Tuomas Aittomäki, Anders Ström, Visa Koivunen

Keywords: Reinforcement Learning, Revisit Interval Selection, Adaptive Update Rate, Radar, Q-learning, Time Budget Management

An adaptive revisit interval selection (RIS) in multifunction radars is an integral part of efficient time budget management (TBM). In this paper, the RIS problem is formulated as a Markov decision problem (MDP) with unknown state transition probabilities and reward distributions. A reward function is proposed to minimize the tracking load (TL) while maintaining the track loss probability (TLP) at a tolerable level. The reinforcement learning (RL) problem is solved using the Q-learning algorithm with an epsilon-greedy policy. Compared to a baseline algorithm, the RL approach was capable of maintaining the tracks while reducing the TL significantly.

157 IEEE RADARCONF 2021 15 ABSTRACTS

An Evaluation of Task and Information Driven Approaches for Radar Resource Allocation Kristine Bell, Chris Kreucher, Muralidhar Rangaswamy

Keywords: Radar Resource Allocation, Tracking, Classification, Cognitive Radar, Fully Adaptive Radar

This paper describes an evaluation of different methods of radar resource allocation. The evaluation operates in a cognitive fully adaptive radar (FAR) framework, specialized to concurrent tracking and classification of multiple airborne targets using a single airborne radar platform. Theframe- work is based on the perception-action cycle of cognition and includes a perceptual processor that performs multiple radar system tasks and an executive processor that allocates system resources to the tasks to decide the next transmission of the radar on a dwell-by-dwell basis. We compare allocation algorithms using task-based and information-based strategies. Our main contribution is the illustration of the allocation algorithms in a MATLAB-based testbed and a comparison of the performance and the sensor task selections made by each.

Tracking and Fusion Begins: 5/13/2021 9:40 Ends: 5/13/2021 11:20 Location: Virtual Room D Chaired by Dale Blair

Exploiting Doppler in Bernoulli Track-Before-Detect Du Yong Kim, Branko Ristic, Luke Rosenberg, Robin Guan, Robin Evans

Keywords: Target Tracking

Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notori- ously challenging compared to classical target tracking problems. Recently, an approach using a Bernoulli track-before-detect (TBD) filter has been proposed for an airborne scanning radar inthe maritime domain. This work focused on a non-coherent fast scanning mode where the dwell-time (time-on-target) was very short. The current paper explores the benefits of using Doppler information in TBD when the radar is operating in a slower scanning mode. A new TBD filter, which exploits the Doppler information in the form of a point measurement is developed and analysed, with significant improvement demonstrated over the traditional fast scanning operation.

Online Multi-Target Tracking for Pedestrian by Fusion of Millimeter Wave Radar and Vision Fucheng Cui, Yuying Song, Jingxuan Wu, Zhouzhen Xie, Chunyi Song, Zhiwei Xu, Kai Ding

Keywords: Multi-target Tracking, Millimeter-wave Radar, Fusion, Multi-hypothesis

A new multi-target multi-sensor tracking algorithm for pedestrians is proposed in this paper to improve the reliability and robustness of tracking under complex autonomous driving scenes, which realizes sensor-fusion tracking by employing a newly proposed back-projection mechanism and a novel multi-hypothesis association approach. To verify the effect of the proposed algorithm, a dataset with 20 sequences is built and used to evaluate tracking performance.

158 IEEE RADARCONF 2021 15 ABSTRACTS

Radar-Aided Navigation System for Small Drones in GPS-Denied Environments Keith Klein, Faruk Uysal, Miguel Caro Cuenca, Matern Otten, Jacco de Wit

Keywords: DSP, Sensor Fusion, Radar-aided Positioning, Navigation, SAR

In scenarios where a GPS signal is unavailable, drift in the onboard sensors causes the inertial navigation system (INS) to quickly deviate from the planned route. This paper proposes a radar-aided navigation method for small drones which is able to estimate the horizontal velocity, and height of the platform independently from GPS. The method is based on an omnidirectional radar system and takes advantage of multi-aspect processing to increase estimation precision. Simulations and experiments verify that drone-based omnidirectional radar can be used to successfully estimate the velocity and course of the platform.

Distributed Registration and Multi-Target Tracking with Unknown Sensor Fields of View Ziting Wang, Lei Chai, Wei Yi, Yongjian Liu

Keywords: PHD, Fields Of View, Registration, WAA Fusion, Multi-Target Tracking

This paper addresses the problem of distributed multi-target tracking in radar network wherein both the positions and fields of view (FoV) of each radar are unknown. The PHD filter and the WAAfusion is employed for distributed multi-target tracking. Due to radar FoVs is unknown, local intensity densities are decomposed by a clustering algorithm. Then the positions of sensors are estimated by minimizing the Cauchy-Schwartz divergence (CSD) between sub-densities which belong to the same cluster. Further, the WAA fusion is performed in parallel sub-densities in each cluster and the fused intensity is constructed as the sum of the fused sub-intensities.

Message Passing Based Extended Objects Tracking with Measurement Rate and Extension Estimation Yuansheng Li, Ping Wei, Yiqi Chen, Yifan Wei, Huaguo Zhang

Keywords: Message Passing, GGIW, Factor Graph, Multiple Extended Objects Tracking

This paper addresses the problem of multiple extended objects (EOs) tracking, which has attracted increasing attention recently due to the developments of high-resolution sensors. Specifically, the state of an EO is modeled by the Gamma-Gaussian-inverse-Wishart (GGIW) distribution, and the message passing inference method, which provides a high efficient implementation for multi- target tracking, is adopted to propagate the posterior of the distributions of multiple EOs. The performance of proposed method is verified via simulation experiments.

Radar Imaging Begins: 5/13/2021 11:40 Ends: 5/13/2021 13:20 Location: Virtual Room A Chaired by Marco Martorella and Brian Rigling

3D-ISAR Using a Single Along Track Baseline Chow Yii Pui, Brian Ng, Luke Rosenberg, Tri-Tan Cao

Keywords: Inverse Synthetic Aperture Radar (ISAR), 3D-ISAR, Interferometric ISAR (InISAR), Temporal ISAR

159 IEEE RADARCONF 2021 15 ABSTRACTS

Inverse synthetic aperture radar (ISAR) has been developed to image non-cooperative targets, so they can be classified by an operator or a more advanced classification system. Three dimensional (3D) ISAR is an extension that produces a different target representation designed to improve the classification results. The technique works by estimating the target scatterersṕosition using measurements from multiple ISAR images. This has previously been achieved using a single receive channel that exploits the temporal motion of the target or with a multi channel dual-baseline interferometric system that uses the phase differences in both the along and across track directions. In this paper, we propose a combination of these two techniques using a single along track baseline suitable for airborne maritime radar systems.

Widely-Distributed Radar Imaging Based on Consensus ADMM Ruizhi Hu, Bhavani Shankar Mysore Rama Rao, Ahmed Murtada, Mohammad Alaee-Kerahroodi, Björn Ottersten

Keywords: ADMM, Artifacts Mitigation, Consensus, Distributed Optimization, Distributed Radar Imaging

In this paper, a novel l1-regularized, consensus alternating direction method of multipliers (CADMM) based algorithm is proposed to mitigate artifacts by exploiting the spatial diversity in a widely- distributed radar system. By imposing the consensus constraints on the local images formed by the distributed antenna clusters and solving the resulting distributed optimization problem, the scenarioś spatially-invariant common features are retained and the spatially-variant artifacts are mitigated in a data-driven fashion, and it will finally converge to a high-quality global image inthe consensus of all widely-distributed measurements. The proposed algorithm outperforms the existing joint sparsity-based composite imaging (JSC) algorithm in terms of artifacts mitigation. It can also reduce the computation and storage burden of large-scale imaging problems through its distributed and parallelizable optimization scheme.

A Hybrid Norm Regularization Approach for Radar Forward-Looking Angle Super-Resolution Imaging Xingyu Tuo, Yin Zhang, Yulin Huang, Jianyu Yang

Keywords: Forward-Looking Radar, Hybrid Regularization Method, Super-Resolution

In order to solve the contradiction between noise suppression and angular resolution improvement of the traditional L1 regularization method, this paper proposes a hybrid L1-L2 regularization superresolution method. In the preprocessing stage, the L2 norm regularization is utilized to smooth the noise and modify the ill-posed property of the deconvolution process. Then an objective function is established as the L1 constraint to increase the angular resolution. Compared with the conventional L2 norm regularization method, the resolution is higher; and the sensitivity to noise is reduced compared with the conventional L1 norm regularization method. Simulation and experimental data results demonstrate the effectiveness of the hybrid norm regularization method.

ISAR Translational Motion Compensation with Simultaneous Range Alignment and Phase Adjustment in Low SNR Environments Jixiang Fu, Mengdao Xing, Moeness G. Amin, Guangcai Sun

Keywords: ISAR, Translational Motion Compensation (TMC), Low SNR

160 IEEE RADARCONF 2021 15 ABSTRACTS

Inverse synthetic aperture radar (ISAR) imaging has been widely developed and applied in many applications, such as, radar target recognition, autopilot and seaport ship surveillance, etc. However, low signal-to-noise ratio (SNR) environments may impede conventional range profiles-based translational motion compensation (TMC), causing image degradation. In this paper, a novel method combining profiles and phase is proposed which utilizes two-dimensional (2-D) coherent accumulation to overcome the low SNR problem, even when the range profiles are submerged into noise. The method only requires Fast Fourier transform (FFT) and Hadamard multiplication operations which makes it suitable for real-time applications.

Efficient Radar Imaging Using Partially Synchronized Distributed Sensors Ahmed Murtada, Ruizhi Hu, Mohammad Alaee-Kerahroodi, Udo Schroeder, Bhavani Shankar Mysore Rama Rao

Keywords: Distributed Radar Imaging, Radar Autofocus, FISTA

In this paper, using a model of unsynchronized distributed radar sensors and a FISTA based reconstruction algorithm, we propose a modified version of the algorithm that allows for automatic selection of regularization parameters based on the easily reconstructed image using the conventional back-projection method, despite being blurred and filled with artifacts. This avoids empirical search enhancing the efficiency of the algorithm. Additionally, we introduce an efficient implementation based on the Fast Fourier Transform (FFT) that exploits the model structure to reduce the algorithm running time as well as its memory requirements compared to direct implementation. The performance of the proposed algorithm is depicted to further increase its attractiveness.

Special Session: Multistatic and Networked Radar - a Tribute to Viktor Chernyak Begins: 5/13/2021 11:40 Ends: 5/13/2021 13:20 Location: Virtual Room B Chaired by Alfonso Farina and Hugh Griffiths

Multistatic and Networked Radar: Principles and Practice Hugh Griffiths, Alfonso Farina

Keywords: Bistatic Radar, Multistatic Radar, Radar Networks

Professor Viktor Chernyak was a visionary whose book Fundamentals of Multisite Radar Systems, published in 1993, set out the principles of multistatic and multiradar systems. This paper sum- marises Chernyak’s contribution, provides some historical background to the development of networked radar, and discusses the technical issues that will be necessary for practical networked radars to be feasible in the future. Application Experience on Radar Networking and Data Fusion Principles Luca Timmoneri, Alfonso Farina, Angela Incardona, Giovanni Golino, Antonio Graziano, Roberto Petrucci, Domenico Vigilante

Keywords: Radar, Networking, Data Fusion

Multi-sensor data fusion is a key infrastructure for both defense and civilian application systems.

161 IEEE RADARCONF 2021 15 ABSTRACTS

By means of suitable fusion algorithms to data from heterogeneous sensors and an optimal sensor management policy, it is possible to obtain better performance than achievable with either single or homogeneous sensors

Advanced Cognitive Networked Radar Surveillance Mohammed Jahangir, Christopher J. Baker, Michail Antoniou, Benjamin Griffin, Alessio Balleri, David Money, Stephen Harman

Keywords: Networks, Multistatic, Distributed, Cognitive, Intelligent, Radar

The concept of a traditional monostatic radar with co-located transmit and receive antennas nat- urally imposes performance limits that can adversely impact applications. Using a multiplicity of transmit and receive antennas and exploiting spatial diversity provides additional degrees of design freedom that can help overcome such limitations. Further, when coupled with cognitive signal processing, such advanced systems offer significant improvement in performance over their monostatic counterparts. This will also likely lead to new applications for radar sensing. In this paper we explore the fundamentals of multistatic network radar highlighting both potential and constraints whilst identifying future research needs and applications.

Drone-Based 3D Interferometric ISAR Imaging Elisa Giusti, Selenia Ghio, Marco Martorella

Keywords: Radar, Radar Imaging, UAV, 3D Imaging, Interferometry, ATR, NCTR

Three-dimensional radar imaging of noncooperative targets has become very attractive as it significantly improves the more traditional two-dimensional radar imaging technology. Drone-based radar systems, on the other end, offer a very flexible, easily deployable and low-cost solution for airborne radar imaging applications. This paper introduces the concept of drone-based 3D radar imaging via an interferometric/ISAR imaging approach. The theoretical work presented in this paper sets the ground for this new technology and simulation results provide some example of the ability of this system to provide 3D radar images of surface moving targets when imaged by a set of drones. Fusion of Local Decisions Based on Rao Test in Resource-Constrained Sensor Networks S. Hamed Javadi, Domenico Ciuonzo

Keywords: Distributed Detection, Generalized Likelihood Ratio Test (GLRT), Rao Test, Threshold Optimization, Wireless Sensor Network

Detection is a basic task of a wireless sensor network (WSN). To meet severe bandwidth and energy limitations of WSNs, network nodes are usually programmed to decide locally about a desired event occurrence and send just one bit to a fusion center (FC) wherein an optimum decision must be taken. In this paper, we employ the Rao test for fusing local decisions of nodes. The Rao test is well-known for its much lower computational complexity than the more common (but computationally heavier) generalized likelihood ratio test (GLRT). We obtain the closed forms of the Rao-test-based fusion rules in both homogeneous and heterogeneous WSNs in the presence of imperfect communication channels. The importance of the presented formulations lies in their simplicity while taking practical issues into account. We show that the Rao-test-based decision fusion in homogeneous WSNs coincides with the counting rule (CR). Moreover, simple methods of

162 IEEE RADARCONF 2021 15 ABSTRACTS adjusting local detection thresholds are proposed. The effectiveness of RAPID in the improvement of the overall network performance is shown through simulations in different scenarios.

Special Session: Digital Array Radar Begins: 5/13/2021 11:40 Ends: 5/13/2021 13:20 Location: Virtual Room C Chaired by Kenneth W. O’Haver and Salvador H. Talisa

Investigation of Beam-Level Nonlinear Equalization in Digital Phased Arrays Robert Schmid, Brian Gibbons, Kenneth O’Haver

Keywords: Nonlinear Equalization, NLEQ, Digital Arrays, Beam-Level NLEQ

Interest in nonlinear equalization (NLEQ) techniques has been growing as a means to reduce nonlinear distortion through digital signal processing rather than more expensive and power-hungry high-linearity analog components. This is especially relevant to element-level digital arrays where there is no spatial filtering before the nonlinear receiver chain. While element-level NLEQ hasshown encouraging performance improvements, an NLEQ implementation at the beam level would be more efficient, requiring less power consumption and a smaller footprint. This work investigates the feasibility of a beam-level approach and compares beam-level NLEQ to more conventional element-level NLEQ. Fully Digital Phased Array Development for Next Generation Weather Radar Matthew Harger, M. David Conway, Henry Thomas, Mark Weber, Alex Morris, Ted Hoffmann, John Bendickson, Nathan Van Schaick

Keywords: Phased Array, Digital, Weather Radar, RF, Systems

The National Oceanic and Atmospheric Administration (NOAA) is evaluating the feasibility of polarimetric phased array radar (PPAR) technology as a basis for its future U.S. operational meteorological radar network. A 10-cm wavelength PPAR – the Advanced Technology Demonstrator (ATD) – is a key asset for exploring associated operational benefits and evaluating solutions to key technical challenges. This paper discusses follow-on PPAR technology development to more fully meet NOAA’s next-generation operational radar requirements. A digital-at-the-element array architecture is recommended to support desired scanning capability, and to maintain data quality commensu- rate with the current operational weather radar. We describe our development of tile-based PPAR prototype technology that exploits novel commercial-off-the-shelf microwave processing technology to meet NOAA’s future requirements. We conclude with an outlook for integration, test, refine- ment and documentation of a 2-panel prototype of this digital PPAR.

Update on an S-Band All-Digital Mobile Phased Array Radar Mark Yeary, Robert Palmer, Caleb Fulton, Jorge Salazar, Hjalti Sigmarsson

Keywords: Radar, Digital Array, MIMO, Digital Beamforming, Polarimetric

This paper provides an update on an S-band, polarimetric phased array radar designed to operate in the 2.7 – 3.1 GHz frequency band, which is being designed and built at the University of Oklahoma’s Advanced Radar Research Center (ARRC). This is radar build 1 of 2 for our group in the S-band. Providing optimum radar flexibility, this phased array radar, known as Horus, is digital atevery

163 IEEE RADARCONF 2021 15 ABSTRACTS element and polarization.

Practical Demonstration of a Self-Calibration Technique Using an Element Level Digital Array Cesar Lugo, Brian Kiedinger, Mitch Miller

Keywords: Digital Array Radar, Self-Calibration, Mutual Coupling Calibration, Element Level Digital Aperture, Phased Array, Phased Array Radar

This paper describes and demonstrates a self-calibration technique for digital arrays that takes advantage of the command and control flexibility afforded by element level digitization. We will show how this technique is able to remove any amplitude or time difference from transmit and receive channels simultaneously. The technique has significant advantages over conventional factory calibration as it is done “on the fly” without the need of external hardware. This means we can continuously calibrate radar systems in the field during mission timelines. The paper will describe the required mathematics and show the technique using a real time element level digital array prototype.

Techniques for Digital Array Radar Planar Near-Field Calibration by Retrofit of an Analog System Thomas Williamson, Jason Whelan, Walter Disharoon, Paul Simmons, Jacob Houck, Brian Holman, Jacob Alward, Killian McDonald, Sean Kim, Dinal Andreasen

Keywords: Digital Array, Digital Array Radar, Digital Beamforming, Planar Near-Field, RFSoC

Digital arrays for radar applications are becoming more common as researchers seek to capitalize on the advantages of digital beamforming, and RF System on Chip (RFSoC) technology matures. Calibrating these digital array radars (DAR) presents new challenges. Primarily, the entire system, from the array face to the digital backend, must be tested as a single unit on these highly integrated designs. Rather than characterizing a system by comparing its analog response to an analog stimulus, engineers must now characterize system performance by comparing a digital response to an analog stimulus. In this paper we will show how a traditional analog planar near-field range (NFR) may be retrofitted to accommodate an RFSoC based DAR operating in the receive mode, address the implementation of controls, discuss clock and timing for DARs in the lab, detail a triggering technique to produce time synchronous data, illustrate post processing techniques for generating far field and hologram plots, and demonstrate the technique with a testbed DAR comprised ofa Xilinx RFSoC operating at L-Band.

Clutter and Target Signatures Begins: 5/13/2021 11:40 Ends: 5/13/2021 13:20 Location: Virtual Room D Chaired by Francesco Fioranelli and Simon Watts

Measurements and Modeling of Heterogeneous Radar Clutter Julie Jackson

Keywords: Heterogeneous Clutter, Clutter Persistence, Radar Measurements

This paper uses measurements made in the AFIT compact range to validate a mixture model for heterogeneous clutter in a coarse resolution cell. Further analysis shows the clutter measurements

164 IEEE RADARCONF 2021 15 ABSTRACTS are correlated over approximately 4 degrees in azimuth. The statistical characterizations in this paper are informative for building target detection and recognition algorithms that support coarse resolution radars, such as those that utilize narrowband communications waveforms. Anisotropic Scatterer Models for Representing RCS of Complex Objects Eric Huang, Coleman Delude, Justin Romberg, Saibal Mukhopadhyay, Madhavan Swaminathan

Keywords: Radar, RCS, High Performance Computing, Point Scatterer Model

High performance computing based emulators can be used to model the scattering from multiple stationary and moving targets for radar applications. These emulators rely on the RCS of the targets being available in complex scenarios. Representing the RCS using tables generated from EM simulations is often times cumbersome leading to large storage requirement. In this paper we present a method to represent the RCS of complex targets using a 3D anisotropic scatterer model, where we use the analytical RCS representation of a large ellipsoid as the basis function to determine the angular dependency of the RCS from each scatterer.

Simulation of Ultra-Wideband Radar Returns from a Notional Sea Surface Jimmy Alatishe

Keywords: Short Pulse, Backscattering Simulation, Sinusoidal Surface, Time-varying Random Air-Dielectric Interface, Ultra-wideband, High Range Resolution

An effort to simulate Ultra-wideband backscattering from a time-varying sinusoidal air-dielectric interface is described. The simulation generates time-dependent high-resolution radar echoes from a surface whose characteristics are represented by the gravitational component of a wind-driven sea. The echoes are computed by using an antenna-reciprocity relationship that accounts for the combined effects of the spatial/temporal coherence of the returns in relation to the antenna radiation characteristics of the illuminated portion of the surface. By employing a surface scattering approximation derived for small surface slopes in the antenna reciprocity relationship, the simulated radar echoes are computed at X-band. Once a coherent-processing interval (CPI) of the data is produced, Range-Doppler and Range-Time-Intensity maps are generated from the simulated returns from the notional sea surface. The results obtained from this investigation will aid in the analysis of the effects of coherence of sea clutter as seen by a monostatic radar. The objective is to further develop the 3D UWB model to further investigate the high-resolution spatial-temporal behavior of sea clutter. The Five-Domain-Six-Map Method for Signal Analysis in Over-the-Horizon Radar Meihui Yan, Zhongtao Luo, Zishu He, Kun Lu

Keywords: OTH Radar, Characteristic Analysis, Clutter And Interference, Five-domain-six-map (5D6M)

This paper proposes a useful method for signal analysis in over-the-horizon (OTH) radar. Based on the time, frequency, range, period, and Doppler domains, six maps are constructed to provide clear observation and comprehensive analysis of OTH radar signals in various domains. The illustration of the five-domain-six-map (5D6M) on the sea-clutter, the radio frequency interference, andthe transient interference demonstrates the effectiveness of the 5D6M method and its advantage on improving the characteristic analysis. Besides, the novel perspectives provided by the 5D6M can

165 IEEE RADARCONF 2021 15 ABSTRACTS help developing new processing algorithms in OTH radar.

Synthetic Aperture Radar Imaging Begins: 5/13/2021 14:20 Ends: 5/13/2021 16:00 Location: Virtual Room A Chaired by Julie Jackson and Laurent Savy

AROMA SAR Refocus of Moving Targets Having Complicated Pitching Maneuvers David Garren

Keywords: Synthetic Aperture Radar, Inverse Synthetic Aperture Radar, Radar Imaging

A recent investigation has yielded an Arbitrary Rigid Object Motion Autofocus (AROMA) methodology that gives automatic refocus of moving targets that have arbitrary variation in the target rotation and translation profiles during the coherent SAR collection time. The current study exam- ines AROMA performance with regards to complicated pitching motions of the target during the SAR collection time. Specifically, this investigation considers the quality of the AROMA refocused imagery for targets that exhibit complicated pitching variation when injected into a background of measured SAR image data.

Differentiable Synthetic Aperture Radar Image Formation and Generalized Minimum Entropy Autofocus Joshua Kantor

Keywords: SAR, Autofocus, Imaging

In this article we describe a novel minimum entropy autofocus framework that can estimate pulse- to-pulse phase and time delay corrections as well as pulse-to-pulse corrections to the sensor trajectory by optimizing image focus. This optimization is accomplished by viewing the image entropy as a composition of the entropy functional and the image formation operator and using the automatic differentiation capabilities of modern machine learning packages to compute the gradient of the objective function, effectively differentiating the full image formation processing chain.

Moving Target Imaging for Synthetic Aperture Radar via RPCA Sean Thammakhoune, Bariscan Yonel, Eric Mason, Birsen Yazici, Yonina Eldar

Keywords: Synthetic Aperture Radar, Robust Principal Component Analysis, Convex, Rank-1, Moving Target Imaging

Synthetic aperture radar (SAR) imaging of moving targets is a challenging task, as standard techniques have been developed for stationary scenes. Motivated by success of robust principal component analysis (RPCA) in change detection for video processing, we establish a rank-1 and sparse decomposition framework for the SAR problem in the image domain. We construct the phase- space reflectivity matrix for single-channel SAR systems by backprojecting at various hypothesized velocities and show that it is the superposition of a rank-1 matrix and a disjoint sparse matrix. This structure allows for additional constraints that reduce the computational complexity when compared to generic RPCA. We compare the performances of two algorithms, proximal gradient descent (PGD) and alternating direction method of multipliers (ADMM), on numerical simulations for the moving target imaging problem.

166 IEEE RADARCONF 2021 15 ABSTRACTS

Rotorcraft-Borne 3-D Forward-Looking MIMO SAR Imaging Jiaying Ren, Jian Li, Lam Nguyen

Keywords: Forward-looking, MIMO, SAR, 3-D SAR Imaging, RELAX, Two-Step Hybrid Algorithm, Pseudo-Polar Format, BP

This paper considers the three-dimensional (3-D) imaging problem for a rotorcraft-borne forward- looking multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) system. We introduce a two-step fast hybrid imaging algorithm to enhance the computational efficiency of the conventional BP algorithm for 3-D MIMO SAR imaging. The proposed fast hybrid imaging algorithm combines the efficient pseudo-polar format imaging approach with the classical BPmethod. We also develop a relaxation-based imaging algorithm, referred to 3DRELAX, for the 3-D forward- looking MIMO SAR Imaging. The effectiveness and efficiency of the proposed imaging algorithms are demonstrated by using several numerical examples of targets at various ranges.

SAR Fast Target Imaging in Sparse Field Based on AlexNet Pan Zhang, Yinger Zhang, Yi Huang, Jiangtao Huangfu, Zhonghe Jin

Keywords: Sparse Field, AlexNet, ROI Target, Raw Echo Signal

Satellite borne Synthetic Aperture Radar (SAR) has become an indispensable means of earth observation since its ability of all-weather and all-weather observation. However, SAR imaging still requires a lot energy and computing resource at present. Nowadays the satellite SAR has been developed greatly in both distributed and intelligent imaging. The traditional satellite SAR imaging method is to process the echo of the received LFM signal in the two-dimensional along the azimuth and distance directions. Due to the targets in the ocean are usually sparsed, it is an effective method to detect the region of interest and targets by analyzing the raw echo signal. In this paper, we propose a method based on convolutional neural network to detect the ROI targets from the raw echo data, so as to achieve rapid detection and imaging of targets. In addition, the new method could greatly improve the target recognition accuracy and imaging efficiency compared with traditional SAR imaging methods.

Multistatic and Distributed MIMO systems Begins: 5/13/2021 14:20 Ends: 5/13/2021 16:00 Location: Virtual Room B Chaired by Mohamed Jahangir and Debora Pastina

A Broadband Multistatic Radar for Trajectory Identification of Multiple Small Caliber Targets Sean Lehman, Jae Jeon, Tammy Chang

Keywords: Broadband Radar, Multistatic, Trajectory, Small Caliber

In recent years, the use of radar for remote sensing has scaled from the detection and tracking of large meter-scale feature sizes to the identification of finer objects and movements. Of particular interest in defense applications is the identification of the trajectories of small caliber projectiles during flight. We present a broadband multistatic radar system for the identification of multiple target trajectories where velocities exceed 300 ms−1. Our radar system is demonstrated at an outdoor firing range in 9-mm caliber live fire experiments. Results indicate that this radarisa promising system for the identification of multiple small, fast-moving object trajectories.

167 IEEE RADARCONF 2021 15 ABSTRACTS

A Fully Modular, Distributed FMCW MIMO Radar System with a Flexible Baseband Frequency Adrian Figueroa, Niko Joram, Frank Ellinger

Keywords: FMCW, MIMO, Radar, System, Harmonic Cancellation, Automatic Calibration

A complete frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar system is presented, incorporating a new and flexible system architecture. The baseband frequency can be freely adjusted by using separate transmit and downconversion chirp waveforms that are generated independently. This leads to various benefits, such as the possibility of removing harmonic distortions. In addition, the adjustability of the radar’s lower distance limit aids in avoiding saturation in the receivers due to close targets or direct coupling. The radar system is fully modular, so that transmitter and receiver units can be added or removed, depending on the application. The radar was tested in a maritime environment and was found to reach a maximum range of 3km for a 50m long ship, and 1km for a 8m long boat. This is achieved with a maximum instantaneous output power of just 30dBm and an antenna gain of 6 dBi. In a 5x15 antenna configuration, the radar offers an average angular resolution of 2.4° and reaches an update rateof10 Hz for the complete radar image.

Information Diversity in Coherent MIMO Radars Salvatore Maresca, Antonio Malacarne, Paolo Ghelfi, Antonella Bogoni

Keywords: MIMO Radar, Geometric Diversity, Frequency Diversity, Ambiguity Function, Coherence, Microwave Photonics

In this paper, the concept of information diversity in both the space and frequency domains is investigated for multiple-input multiple-output (MIMO) radars with widely separated antennas. This analysis proceeds in parallel with the progresses of microwave photonics (MWP), which could soon become a new paradigm for the development of centralized MIMO radar architectures. Thus, understanding the implications of information diversity becomes essential to foretell system capabilities. Performance metrics are proposed and evaluated to characterize the effects that information diversity have on MIMO radars. On the other hand, the proposed methodology is precious for designing the optimum system configuration.

Antenna Placement for Distributed MIMO Radar with Different Missions in Different Subareas Yao Wang, Wei Yi, Lingjiang Kong

Keywords: Distributed MIMO Radar, Antenna Placement, Subarea Performance, Multi-Objective Optimization

We consider an antenna placement optimization problem for distributed multi-input multi-output (MIMO) radar systems in this paper. We aim to enhance the system surveillance and localization performance in different subareas by optimizing antenna positions. Firstly, performance metrics are formulated to evaluate the surveillance and localization performance of the distributed MIMO radar systems. However, the proposed optimization problem is high-dimensional, nonlinear, and especially multi-objective due to two objective functions conflicting with each other. To solve this problem, we further propose an enhanced multi-objective particle swarm optimization (MOPSO) algorithm, which differs from the traditional MOPSO in that its particle can properly consider the differences between the elements within the particle. Finally, simulation results are provided to verify the effectiveness of the proposed algorithm.

168 IEEE RADARCONF 2021 15 ABSTRACTS

Rx Beamforming for Long Baseline Multistatic Radar Networks Rudolf Hoffmann, Nadav Neuberger, Risto Vehmas

Keywords: Distributed Radar, Radar Networks, Multistatic Radar, Digital Beamforming, Bistatic Radar, Position Estimation

Distributed phased array radar networks provide an improved target detection and parameter estimation performance due to extended spatial coverage and multiple observation perspectives. By increasing a networkś baseline, these capabilities can be further enhanced. However, the spatial area that the Rx stations need to cover increases with growing baselines, requiring a high number of Rx beams. This poses a challenge, since the number of receiving channels is often limited by hardware or software. Therefore, designing the Rx beamformer is a key factor for baseline extension and network performance. In this paper, we analyze the use of two different beamformers for a bistatic network configuration: a commonly used sum beam method and a recently proposed eigenbeamformer. The numerical results demonstrate the superiority of the eigenbeamformer in parameter estimation accuracy, resource efficiency and baseline extension ability.

Antennas and Components Begins: 5/13/2021 14:20 Ends: 5/13/2021 16:00 Location: Virtual Room C Chaired by Alan Fenn and Daniel Rabideau

Multi-Channel Feedarray Reflector Antenna Based Radar Concept for HRWS SAR Imaging Javier del Castillo, Lara Orgaz, Quiterio Garcia, Nafsika Memeletzoglou, Carlos Biurrun-Quel, Carlos del-Río, Giovanni Toso, Ernesto Imbembo

Keywords: Synthetic Aperture Radar, Digital Beamforming

The presented work shows the design of a multichannel L-band SAR system and antenna architecture based on different feeders techniques which is suitable for next generation L-band SAR missions and capable to map 400 km ground swath with 5 meter spatial resolution. The paper describes a preliminary design and architecture of the SAR system and antenna where different design aspects including beamforming concepts and feedarray implementation techniques have been identified and assessed in view of achieving the required performances while reducing the overall complexity of the solution

The New Water-Cooled Cold Plate for Active Phased Array Antenna Using AM Technology Toshihiro Kitazaki, Naoya Akaishi, Shigenao Tomiyasu, Genki Honma

Keywords: Antenna Arrays, Electronics Cooling power of active phased array antenna and heat of the antenna element tend to increase. therefore, a cooling system with higher efficiency for the antenna element is required. the new water-cooled cold plate we developed using am technology must be an important component in the antenna system. the new water-cooled cold plate has significantly improved heat dissipation performance compared to conventional manufacturing technology.

169 IEEE RADARCONF 2021 15 ABSTRACTS

A 2-Stage GaN IMFET Power Amplifier in an Embedded Heat Slug Laminate Bo Zhao, Christopher Sanabria, Terry Hon, Alex Arayata

Keywords: IMFET, EHS, PAE, Power Amplifier (PA), GaN, Air-cavity, S-Band, Discrete, Laminate

This paper describes a new high power high PAE high integration, 2-stage, S-band internally matched GaN HEMT FET power amplifier (IMFET PA) QPA2513 in an air-cavity laminate module. QPA2513 uses Qorvo’s high performance 50 V, 0.25 um, GaN-on-SiC process technology. Dimensions of the complete PA module are 25 mm x 12.5 mm x 3.488 mm. This PA delivers a typical 138 W pulsed power, 67.5 % Power added efficiency (PAE) and 31.5 dB compressed gain. It is the first reported 2-stage IMFET GaN PA in a laminate package.

Design of a High-Order Dual-Wideband Superconducting Filter Using Stepped-Impedance Cross Structures Xilong Lu, Weihua Wang, Yuhua Zhang, Delong Fu

Keywords: Broad Dual-Band Filter, High-Temperature Superconducting, Stepped-Impedance Cross Structure

In this letter, the stepped-impedance cross structure (SICS) is proposed on superconducting films, to design a dual-wideband filter. The proposed stepped-impedance cross structure (SICS) isde- tailed theoretically analyzed, and the two passband center frequencies can be adjusted independently. A high-order dual-band filter is designed and fabricated with this structure, the couplings of the high-band and low-band filters can also be controlled. Transmission zeroes are introduced based on this coupling structure to improve selectivity.

Enhancing Frequency-Agile Radar Range Over a Broad Operating Bandwidth with Reconfigurable Transmitter Amplifier Matching Networks Justin Roessler, Adam Goad, Austin Egbert, Charles Baylis, Anthony Martone, Robert J Marks II, Benjamin Kirk

Keywords: Broadband Amplifiers, Tunable Circuits And Devices, Impedance Matching, Cognitive Radar

Tunable narrowband impedance matching networks (MNs) can be used to optimize radar range in real time after operating-frequency changes in a spectrum sharing radar’s transmitter. This paper compares two similar power-amplifier designs over an operating range of 3.1GHz to 3.5GHz: one with a fixed broadband output MN and the other with a tunable narrowband output MN. Simulations show that the design with the tunable output MN achieves greater than 2dB more gain across the frequency range than the fixed-broadband MN design. This allows a significant increase inradar range, with reconfiguration time being the only penalty for using a reconfigurable MN.

170 IEEE RADARCONF 2021 15 ABSTRACTS

Ground-Penetrating and Sounding Begins: 5/13/2021 14:20 Ends: 5/13/2021 16:00 Location: Virtual Room D Chaired by Lorenzo Lo Monte and Mark Yeary

Development of a UAS-Based Ultra-Wideband Radar for Fine-Resolution Soil Moisture Measurements Christopher Simpson, Shriniwas Kolpuke, Abhishek Awasthi, Tuan Luong, Sama Memari, Jie-Bang Stephen Yan, Ryan Taylor, Jordan Larson, Prabhakar Clement

Keywords: UAS Radar, Soil Moisture Radar, FMCW Radar

The Remote Sensing Center at the University of Alabama has developed a compact, ultra-wideband microwave radar operating over the frequency range of 2-6 GHz for airborne soil moisture measurements. The radar performance is verified using simulated targets in the laboratory, and the measured impulse response is close to the ideal response. We have integrated the radar with a hybrid multirotor unmanned aircraft system (UAS) that can be operated within FAA limits. We have operated the radar and collected data at test sites near Tuscaloosa, AL. In this paper, we will discuss the design and development of the radar, measurements, and field results.

Experimental Study on the Detection of Avalanche Victims Using an Airborne Ground Penetrating Synthetic Aperture Radar Alexander Grathwohl, Philipp Hinz, Ralf Burr, Maximilian Steiner, Christian Waldschmidt

Keywords: Avalanche Victims Localization, FMCW, GPR, SAR, Unmanned Aerial System

For avalanche victims a short burial time is imperative for a good chance of survival. Although airborne radar systems have been used for a long time in monitoring snow accumulation, their ability to quickly cover large areas of terrain is however not commonly employed in the search for buried victims. The presented method for detection and localization of avalanche victims utilizes a ground penetrating radar mounted on an unmanned aerial system. In order to detect buried victims, the system is operated as a synthetic aperture radar using frequency-modulated continuous-wave modulation. Therefore, it is not dependent on avalanche beacons. This approach is successfully demonstrated in an experimental study, where buried, water-filled mannequin torsos could be detected in fresh as well as compressed mixed snow in a realistic winter environment on 1900m above mean sea level. An Improved Borehole Radar Fusion-Imaging Method for Heterogeneous Subsurface Sensing Haining Yang, Shijia Yi, Na Li, Tingjun Li, Yujian Cheng, Qinghuo Liu

Keywords: Borehole Radar, Radar Imaging, Fusion Before Imaging

In this paper, a heterogeneous borehole radar fusion-before-imaging method (RFBI) is proposed and validated. Different from traditional fusion methods, RFBI firstly fuses radar sample sets from heterogeneous radar systems and then do imaging process once in high-dimensional space to obtain imaging result with high accuracy. In the fused high-dimensional sample space of RFBI, the data diversity and redundancy are utilized, which brings the benefit of high imaging accuracy. Meanwhile, all radar sampling sets are imaged once, which enable RFBI owns high efficiency. The comparison results with conventional methods indicates that RFBI is more suitable for heteroge-

171 IEEE RADARCONF 2021 15 ABSTRACTS neous radar imaging applications.

A Probe-Mounted Radar Downward-Looking Mapping Method for Mars Exploration Xue Peng, Yongchao Zhang, Yin Zhang, Yulin Huang, Haiguang Yang, Jianyu Yang

Keywords: Mars Exploration, Radar Imaging, Downward-Looking, MUSIC Algorithm

Limited by Mars’s complicated conditions, it‘s difficult to realize all-time and all-weather landing guidance based on optical instrument. To solve this problem, we propose a radar downward- looking method, allowing for mapping the surface of Mars in the stage of probe hovering. To this end, we firstly establish a radial downward-looking echo mode. Then, we incooperate the MUSIC method with the proposed model to reconstruct the super-resolution surface map of Mars. Com- pared with other imaging methods, the proposed method make up for the blind area, and improve the safe landing capability for the Mars rover in the complex environment.

172 Author Index

Aboutanios, Elias, 111 Bendickson, John, 163 Abratkiewicz, Karol, 135 Bilik, Igal, 108, 143 Adve, Raviraj, 137 Biurrun-Quel, Carlos, 169 Ahmed, Sherif, 124 Blair, William Dale, 152 Aittomäki, Tuomas, 157 Blake, William, 122 Akaishi, Naoya, 169 Blakely, Jonathan, 117 Akhtar, Airas, 149 Bliss, Daniel W., 130 Alaee-Kerahroodi, Mohammad, 160, 161 Blum, Rick S., 112 Alatishe, Jimmy, 165 Blunt, Shannon, 114 Albuquerque, Heitor, 125 Bocus, Mohammud, 155 Ali, Touseef, 112 Bogoni, Antonella, 168 Alp, Yasar Kemal, 142 Bongioanni, Carlo, 129 Altiparmak, Fatih, 142 Bourdoux, André, 115, 135 Alward, Jacob, 164 Brandewie, Aaron, 156 Amin, Moeness G., 131, 137, 160 Brüggenwirth, Stefan, 120, 121 Andonovic, Ivan, 137 Bu, Yi, 114, 115, 133 Andreasen, Dinal, 164 Buehrer, Michael, 133, 140 Anitori, Laura, 106 Bufler, Travis, 114 Ankel, Martin, 118 Burger, Isaiah, 122 Antoniou, Michail, 121, 124, 162 Burkholder, Robert, 156 Apfeld, Sabine, 141 Burr, Ralf, 171 Arayata, Alex, 170 Buyukaksoy Kaplan, Gulay, 122 Arbabian, Amin, 124 Arikan, Orhan, 139 Cabrera, Octavio, 129 Ascheid, Gerd, 141 Calderbank, Robert, 118 Atalik, Arda, 139 Cao, Changjie, 123 Atkinson, George, 121 Cao, Rui, 145 Awadhiya, Rajat, 105 Cao, Ruisong, 138 Awasthi, Abhishek, 171 Cao, Tri-Tan, 159 Aydogdu, Canan, 109 Cao, Zongjie, 123 Caro Cuenca, Miguel, 159 Baker, Christopher J., 121, 124, 162 Carr, Caleb, 134 Balaji, Bhashyam, 110 Carvajal, Gisela K., 109 Balleri, Alessio, 152, 162 Casgrain, Catherine, 147 Bartusch, Michael, 147 Chai, Lei, 159 Basrawi, Khaled, 144 Chalise, Batu, 112 Bauduin, Marc, 115, 135 Chang, Tammy, 167 Bauer, Max Paul, 116 Chardon, Gilles, 155 Baylis, Charles, 133, 140, 170 Charlish, Alexander, 107, 141 Bekar, Ali, 124 Chen, Genshe, 128 Bell, Kristine, 158 Chen, Pengyun, 127, 135 Bell, Muyinatu, 141 Chen, Tianyi, 116 Beluch, William, 142 Chen, Yiqi, 159 Benavidez, Edward, 138 Cheng, Yujian, 171 Benavoli, Alessio, 152 Chetty, Kevin, 120, 137, 155

173 IEEE RADARCONF 2021 AUTHOR INDEX

Chopard, Adrien, 123 Ebelt, Randolf, 117 Christiansen, Jonas Myhre, 124 Egbert, Austin, 133, 140, 170 Ciuonzo, Domenico, 162 Eldar, Yonina, 166 Clement, Prabhakar, 171 Ellinger, Frank, 168 Clemente, Carmine, 127, 137 Ender, Joachim, 106 Cogun, Fuat, 142 Eriksson, Olof, 109 Colone, Fabiola, 129 Evans, Robin, 158 Connors, Charles, 154 Conway, M. David, 163 Fan, Tao, 105, 115, 126, 133 Correll Jr, Bill, 114 Farhadi, Masoud, 115 Cozma, Adriana-Eliza, 142 Farina, Alfonso, 110, 152, 161 Crawford, Chris, 130, 131 Farnham, Tim, 143 Cristallini, Diego, 148, 149 Fauquet, Frédéric, 123 Cui, Fucheng, 158 Feger, Reinhard, 115 Cui, Guolong, 105, 114, 115, 126, 127, 133, Feintuch, Stefan, 143 135, 138, 139 Feltes, Alex, 125 Cui, Han, 143 Feng, Lifang, 139 Cui, Zongyong, 123 Feng, Yi, 118 Czerkawski, Mikolaj, 137 Feng, Yun, 132 Côté, Stephane, 147 Feuillen, Thomas, 106 Figueroa, Adrian, 168 Dadon, Yonatan David, 143 Filippini, Francesca, 129 Dale, Holly, 121 Fink, Johannes, 115 Dammann, Armin, 153 Fioranelli, Francesco, 124, 126, 128, 136 Dang, Bibi, 134 Foreman, Terry, 113 Das, Aishwarya, 143 Fortier, Réjean, 147 Dasgupta, Soura, 131 Frasca, Marco, 110 Daum, Fred, 111 Fu, Delong, 170 Davidson, Malcolm, 146 Fu, Jixiang, 160 De Lisle, Daniel, 147 Fuchs, Alexander, 127 de Wit, Jacco, 159 Fuentes-Michel, Juan-Carlos, 143 del Castillo, Javier, 169 Fulton, Caleb, 163 Del Galdo, Giovanni, 105 Furgerson, Jase, 157 del-Río, Carlos, 169 Furnell, Robert, 146 Deligiannis, Anastasios, 143 Delude, Coleman, 165 Gambi, Ennio, 136 DeMartinis, Guy, 138 Garcia, Quiterio, 169 Deng, Hai, 134 Garren, David, 166 Devault, Jordan, 134 Gatesman, Andrew, 138 Dhulashia, Dilan, 120 Ge, Mengmeng, 138 Dill, Richard, 144 Gebert, Nico, 146 Ding, Kai, 148, 158 Gentner, Christian, 153 Disharoon, Walter, 164 Georgiev, Krasin, 112 Dogancay, Kutluyil, 113 Geudtner, Dirk, 146 Doufexi, Angela, 143 Ghelfi, Paolo, 168 Droszcz, Aleksander, 155 Ghio, Selenia, 153, 162 du Plessis, Warren, 145 Gibbons, Brian, 163 Durst, Sebastian, 120 Gielen, Thomas, 135

174 IEEE RADARCONF 2021 AUTHOR INDEX

Giusti, Elisa, 153, 162 Hong, Bingqing, 151 Gjermo Chomitz, Hanna, 118 Honma, Genki, 169 Gläser, Claudius, 142 Houck, Jacob, 164 Goad, Adam, 140, 170 Howard, William, 133 Gogineni, Sandeep, 118 Hu, Jinfeng, 119, 132 Goh, Jing Shun, 123 Hu, Ruizhi, 160, 161 Golino, Giovanni, 161 Hu, Rujun, 114, 138 Gorji, Ali, 135 Huang, Eric, 165 Gosain, Tanisha, 129 Huang, Jim, 111 Govoni, Mark, 132 Huang, Yi, 167 Grathwohl, Alexander, 171 Huang, Yongming, 138, 139 Graziano, Antonio, 161 Huang, Yulin, 119, 136, 139, 154, 160, 172 Griffin, Benjamin, 162 Huangfu, Jiangtao, 167 Griffin, Darrin, 130, 131 Huo, Weibo, 136 Griffiths, Hugh, 107, 161 Huston, Dryver, 145, 150 Gromek, Artur, 155 Guan, Robin, 158 Ilioudis, Christos, 127, 137 Imbembo, Ernesto, 169 Guendel, Ronny Gerhard, 136 Incardona, Angela, 161 Guillet, Jean-Paul, 123 Guo, Shisheng, 135 Jackson, Julie, 164 Guo, Zhengwei, 125 Jackson, Julie Ann, 118 Gupta, Debayan, 143 Jacques, Laurent, 106 Gurbuz, Ali Cafer, 130, 131 Jahangir, Mohammed, 121, 162 Gurbuz, Sevgi Zubeyde, 124, 129–131, 137 Javadi, S. Hamed, 162 Gusland, Daniel, 124 Jeon, Jae, 167 Jeong, Nathan, 109 Hamza, Syed A., 131 Jian, Qiang, 135 Han, Chenggao, 106 Jiang, Chenming, 116 Harger, Matthew, 163 Jin, Long, 145 Harman, Stephen, 162 Jin, Yi, 143 Harris, Garrett, 154 Jin, Zhonghe, 167 Hartas, Mike, 113 Jones, Christian, 114 Hasch, Jürgen, 109 Jonsson, Robert, 118 Hazra, Souvik, 130 Joram, Niko, 168 He, Qian, 112, 113 Julier, Simon, 137 He, Zishu, 165 Jędrzejewski, Konrad, 155 Hebert, Daniel, 119 Heliere, Florence, 146 Kahl, Justin, 131 Hellsten, Hans, 109 Kang, Bosung, 118 Herbertsson, Hans, 109 Kankaku, Yukihiro, 147 Higgins, Thomas, 114 Kantor, Joshua, 166 Himed, Braham, 134 Karsa, Athena, 111 Hinz, Philipp, 171 Karásek, Rostislav, 153 Hoffmann, Marcel, 117 Kaya, Engin, 122 Hoffmann, Rudolf, 169 Kazemi, Samia, 121 Hoffmann, Ted, 163 Keel, Byron, 152 Holman, Brian, 164 Keskin, Musa Furkan, 109 Hon, Terry, 170 Khan, Aftab, 143

175 IEEE RADARCONF 2021 AUTHOR INDEX

Kiedinger, Brian, 164 Liu, Ruitao, 139 Kim, Du Yong, 158 Liu, Shengheng, 138, 139 Kim, Sean, 164 Liu, Xiaoyu, 154 Kirk, Benjamin, 134, 170 Liu, Yongjian, 159 Kitazaki, Toshihiro, 169 Lombardo, Pierfrancesco, 129 Klein, Keith, 159 Longman, Oren, 108 Knill, Christina, 109 Lops, Marco, 149 Kohler, Michael, 108 Lu, Kun, 165 Koivunen, Visa, 157 Lu, Qinghui, 139 Kolpuke, Shriniwas, 171 Lu, Xilong, 170 Kong, Lingjiang, 168 Lugo, Cesar, 164 Kong, Yukai, 126 Lukin, Konstantin, 110 Kotterman, Wim, 105 Luo, Zhongtao, 165 Kovarskiy, Jacob, 134 Luong, David, 110 Kreucher, Chris, 158 Luong, Tuan, 171 Kroupnik, Guennadi, 147 Lv, Zongsen, 125 Kulpa, Krzysztof, 135, 155 Kumar, Pratyush, 143 Malacarne, Antonio, 168 Kumar, Raj, 146 Malaia, Evie, 130, 131 Kurtoglu, Emre, 131 Malanowski, Mateusz, 155 Kłos, Julia, 155 Mani, Anil, 108 Mao, Deqing, 119 Lapointe, Mélanie, 147 Mao, Zihuan, 138, 139 Larson, Jordan, 171 Maresca, Salvatore, 168 Latham, Casey, 133 Marks II, Robert J, 133, 140, 170 Le Kernec, Julien, 128 Martone, Anthony, 107, 133, 134, 140, 170 Lehman, Sean, 167 Martorella, Marco, 153, 162 Leitinger, Erik, 151 Mason, Eric, 166 Lewis, Benjamin, 120 McCargar, Reid, 157 Li, Bingcheng, 136 McCleary, James, 127 Li, Dongsheng, 145 McDonald, Killian, 164 Li, Hongbin, 132 Mdrafi, Robiulhossain, 130 Li, Hu, 151 Mehboob, Azam, 113 Li, Jian, 167 Meinecke, Benedikt, 109 Li, Jie, 119 Meissner, Paul, 127 Li, Junjie, 148 Meller, Michał, 141 Li, Na, 171 Memari, Sama, 171 Li, Ning, 125 Memeletzoglou, Nafsika, 169 Li, Peizheng, 143 Metcalf, Justin, 134 Li, Siqi, 132 Mete, Mustafa, 109 Li, Tingjun, 171 Meyer, Florian, 151 Li, Wenda, 137 Michev, Rossen, 109 Li, Xiaolong, 105 Michie, Craig, 137 Li, Yi, 128 Miller, Mitch, 164 Li, Yuansheng, 159 Mishra, Kumar Vijay, 130 Li, Zhenghui, 128 Misiurewicz, Jacek, 155 Lievsay, James, 118, 148 Miura, Satoko, 147 Liu, Qinghuo, 171 Money, David, 162

176 IEEE RADARCONF 2021 AUTHOR INDEX

Monnoyer de Galland, Gilles, 106 Pei, Jifang, 136, 154 More, Yash, 143 Peng, Xue, 172 Moreira, Alberto, 147 Permuter, Haim Henry, 143 Morris, Alex, 163 Pernkopf, Franz, 127 Moss, Byrant, 113 Peter, Soorya, 122 Motohka, Takeshi, 147 Petrucci, Roberto, 161 Mottier, Manon, 155 Pezeshki, Ali, 118 Mounaix, Patrick, 123 Pfeiffer, Michael, 142 Mukhopadhyay, Saibal, 165 Pi, Yiming, 123 Mulgrew, Bernard, 150 Piechocki, Robert, 143, 155 Murtada, Ahmed, 160, 161 Pieterse, Frans-Paul, 145 Myint, Saw James, 105 Pirandola, Stefano, 111 Mysore Rama Rao, Bhavani Shankar, 160, Pisciottano, Iole, 149 161 Power, Raymond, 126 Pożoga, Mariusz, 155 Nair, Arun, 141 Priyadarshi, Vedansh, 143 Naraghi-Pour, Mort, 156 Prophet, Robert, 143 Narayanan, Ram, 114, 134 Pui, Chow Yii, 159 Navas Traver, Ignacio, 146 Pulkkinen, Petteri, 157 Neuberger, Nadav, 169 Newey, Michael, 154 Qian, Junhui, 149 Ng, Brian, 113, 159 Qiu, Hui, 115, 133 Ng, Yuting, 118 Qiu, Linfeng, 139 Ngo, Anthony, 116 Nguyen, Lam, 141, 167 Rahman, Mohammad Mahbubur, 129, 130, Ni, Tianheng, 139 137 Niazi, Usman, 130 Rajan, Dinesh, 157 Nilsson, Emil, 109 Rajan, Sreeraman, 110 Noushin, Arjang, 111 Ram, Shobha, 129 Nuncio Quiroz, Elizabeth, 147 Ramachandran, Umakishore, 152 Nussbaum, Alan, 152 Rambach, Kilian, 142 Rangamani, Akshay, 141 O’Hagan, Daniel, 108 Rangaswamy, Muralidhar, 118, 158 O’Haver, Kenneth, 163 Rao, Sandeep, 108 Orfeo, Daniel, 145, 150 Rashid, Mohammed, 156 Orgaz, Lara, 169 Ravenscroft, Brandon, 114 Osadciw, Lisa, 119 Raza, Usman, 143 Otten, Matern, 159 Ottersten, Björn, 160 Reddy, Vinod, 122 Oveis, Amir Hosein, 153 Reininger, Taylor, 127 Ozmen, Emirhan, 142 Ren, Jiaying, 167 Ren, Yehan, 136 Palmer, Robert, 163 Resch, Michael, 116 Pan, Mingming, 123 Richmond, Christ, 112 Panati, Chandana, 121 Rincon, Rafael, 146 Parra García, Laura, 127 Ristic, Branko, 158 Pascal, Frédéric, 155 Ritchie, Matthew, 120, 124 Pastina, Debora, 149 Rock, Johanna, 127 Patel, Kanil, 142 Roessler, Justin, 170

177 IEEE RADARCONF 2021 AUTHOR INDEX

Rohde, Jann, 150 Sun, Guangcai, 160 Romain, Olivier, 128 Sun, Jun, 156 Romberg, Justin, 165 Sun, Shunqiao, 109 Romero, Ric, 125, 150 Sun, Zhi, 105 Rong, Yu, 130 Suntharalingam, Sureshan, 148 Rosen, Paul, 146 Suzuki, Shinichi, 147 Rosenberg, Luke, 111, 113, 148, 158, 159 Svenningsson, Peter, 126 Rydström, Mats, 109 Swaminathan, Madhavan, 165 Swanson, Christopher N., 114 Sahli, Hichem, 135 Salazar, Jorge, 163 Taberkian, Joseph, 143 Samczyński, Piotr, 135 Tachibana, Yuki, 106 Sanabria, Christopher, 170 Tachtatzis, Christos, 137 Santi, Fabrizio, 149 Tang, Chong, 137 Santra, Avik, 130 Tarokh, Vahid, 118 Scarnati, Theresa, 120, 154 Taylor, Ryan, 171 Schieler, Steffen, 105 Tertinek, Stefan, 151 Schmid, Robert, 163 Thammakhoune, Sean, 166 Schneider, Christian, 105 Thomas, Daniel, 107 Schroeder, Udo, 161 Thomas, Henry, 163 Schwarz, Chris, 131 Thomä, Reiner, 105 Schweizer, Benedikt, 109 Thornton, Charles, 133, 140 Schüßler, Christian, 117 Timm, Fabian, 142 Sgroi, Fabio, 109 Timmoneri, Luca, 161 Shapiro, Jeffrey, 117 Toker, Onur, 144 Sharma, Ankit, 116 Tomiyasu, Shigenao, 169 Sharma, Prafull, 154 Torres, Ramon, 146 Sherbondy, Kelly, 134 Torvik, Børge, 124 Shontz, Suzanne, 114 Toso, Giovanni, 169 Sigmarsson, Hjalti, 146, 163 Tossaint, Michel, 146 Sil, Dibakar, 116 Toth, Mate, 127 Simmons, Paul, 164 Tran, Trac, 141 Simonov, Anton, 123 Tuo, Xingyu, 160 Simpson, Christopher, 171 Ulmschneider, Markus, 153 Singh, Aditya, 143 Ulrich, Michael, 142 Singh, Nihal, 116 Unterhorst, Matteo, 136 Smith, Graeme, 127, 157 Uysal, Faruk, 159 Smolyanskaya, Olga, 123 Soltani, Mohammadreza, 118 Van Schaick, Nathan, 163 Song, Chunyi, 148, 158 Vandendorpe, Luc, 106 Song, Yuying, 158 Vanäs, Karl, 109 Steiner, Maximilian, 171 Vehmas, Risto, 105, 169 Stelzer, Andreas, 115 Venturino, Luca, 149 Stenger, Peter, 126 Venus, Alexander, 151 Stettner, Samuel, 147 Vigilante, Domenico, 161 Stralka, John, 107 Villeval, Shahar, 108 Ström, Anders, 157 Vishwakarma, Shelly, 120, 137 Summers, Randall, 146 Vogel, James, 114

178 IEEE RADARCONF 2021 AUTHOR INDEX

Vossiek, Martin, 117, 143 Yamin, Shahaf, 143 Yan, Jie-Bang Stephen, 171 Wagner, Kevin, 112, 114 Yan, Meihui, 165 Wagner, Simon, 121 Yan, Zhengxin, 114, 138 Wagner, Thomas, 115 Yang, Bin, 116, 142 Waldschmidt, Christian, 109, 171 Yang, Chengxin, 156 Wang, Chenwei, 154 Yang, Haiguang, 172 Wang, Chenyu, 105 Yang, Haining, 171 Wang, Dandan, 145 Yang, Jianyu, 119, 136, 139, 154, 160, 172 Wang, Fangzhou, 132 Yang, Shufan, 128 Wang, Liying, 123 Yarovoy, Alexander, 126, 136 Wang, Mingxing, 105, 126 Yazici, Birsen, 121, 149, 166 Wang, Pengfei, 132 Ye, Shabing, 113 Wang, Weihua, 170 Yeary, Mark, 146, 163 Wang, Wenqin, 151 Yi, Shijia, 171 Wang, Xiang, 127 Yi, Wei, 156, 159, 168 Wang, Xiangrong, 131 Yilmaz, Mustafa, 139 Wang, Xiaodong, 149 Yonel, Bariscan, 149, 166 Wang, Xiaorui, 113 Yu, Xianxiang, 114, 115, 126, 132, 133, 138, Wang, Yao, 168 139 Wang, Zhen, 112 Wang, Ziting, 159 Zainab, Hunza, 131 Ward, Euan, 150 Zeng, Cengcang, 132 Weber, Ingo, 117, 143 Zhai, Weitong, 131 Weber, Mark, 163 Zhang, Dalin, 156 Wei, Junkang, 148 Zhang, Huaguo, 159 Wei, Ping, 159 Zhang, Jingzhi, 124 Wei, Yaqi, 156 Zhang, Lei, 128 Wei, Yifan, 159 Zhang, Liwei, 126 Weigel, Robert, 130 Zhang, Pan, 167 Werbunat, David, 109 Zhang, Qiping, 119, 132 Whelan, Jason, 164 Zhang, Weijian, 119, 132 Williamson, Thomas, 164 Zhang, Wenyu, 151 Witrisal, Klaus, 151 Zhang, Yan, 145 Wojaczek, Philipp, 148 Zhang, Yimin, 128 Wong, Malcolm, 111 Zhang, Yin, 139, 160, 172 Wongkamthong, Chayut, 118 Zhang, Yinger, 167 Worms, Josef, 108 Zhang, Yongchao, 119, 139, 172 Wu, Jingxuan, 158 Zhang, Yongwei, 119 Wu, Peilun, 135 Zhang, Yuhua, 170 Wu, Yining, 138 Zhao, Bo, 170 Zhao, Jianhui, 125 Xia, Tian, 145, 150 Zhong, Kai, 132 Xiang, Xingyu, 128 Zhou, Qiyu, 132 Xie, Hangchen, 127 Zhu, Hongliang, 153 Xie, Zhouzhen, 158 Zhu, Jinghui, 139 Xing, Mengdao, 160 Zink, Manfred, 147 Xu, Lifan, 109 Zong, Zhulin, 132 Xu, Zhiwei, 148, 158 Zou, Xinying, 119

179 Keyword Index

1-Norm, 131 Asynchronous Propagation, 132 2DPCA, 138 ATR, 120, 154, 162 3-D SAR Imaging, 167 Autofocus, 166 3D Imaging, 162 Automatic Calibration, 168 3D-ISAR, 159 Automatic Modulation Recognition, 136 4G LTE Communications, 134 Automatic Target Recognition, 121 77GHz, 143 Automotive, 131, 142 Automotive Radar, 108, 109, 116, 117, 127, Accelerating Targets, 152 142, 143 Active Phased Array Antenna, 147 Automotive SAR, 115 Activity Recognition, 137, 155 Autonomous Driving, 109 AD, 109 Avalanche Victims Localization, 171 Adaptive Beam-Forming, 132 Average Likelihood Ratio Test (ALRT), 112 Adaptive RF/analog Cancellation, 126 Adaptive Signal Processing, 119 Backscattering Simulation, 165 Adaptive Update Rate, 157 Bats, 112 Adaptive Waveform Design, 152 Bayesian Methods, 141 ADAS, 109 Beam-Level NLEQ, 163 ADMM, 160 Beamforming, 139 Adversarial Neural Network, 116 Beampattern, 133 Adversarial Neural Networks, 129 Belavkin-Zakai Equation, 111 AI, 143 Belief Propagation, 151 Air-cavity, 170 Bhattacharya Distance, 117 Airborne, 148 Biconvex Optimization, 119 Airborne Forward-Looking Radar, 119 Binary Linear Program, 157 Airborne Radar, 118 BIOMASS, 146 Airborne Weather Radar, 122 Birds, 121 AlexNet, 167 Bistatic Passive Radar, 156 Algorithm With Sparsity Prior, 149 Bistatic Radar, 161, 169 ALOS, 147 Bistatic RCS, 105 Alternating Direction Methods Of Multiplier, Bistatic SAR, 147 119 Black Box Variational Inference, 141 Ambiguity Function, 168 Borehole Radar, 171 And Phase Errors, 132 BP, 167 Android, 144 Bridge Mixer, 145 Antenna Arrays, 169 Broad Dual-Band Filter, 170 Antenna Placement, 168 Broadband Amplifiers, 170 Antenna Selection, 139 Broadband Radar, 167 Approximation Errors, 119 BSCT, 112 APS, 115 BSS, 138 Array Gain, 139 Artifacts Mitigation, 160 CCD, 139 Artificial Intelligence (AI), 134 Change Detection Algorithms, 140 ASL, 130 Characteristic Analysis, 165 Assisted Living, 136 Chirp Waveform, 106

180 IEEE RADARCONF 2021 KEYWORD INDEX

CIR, 155 Covariance Matrix, 139 Circuit Optimization, 140 CPCL, 105 Classification, 121, 122, 142, 143, 158 Cramer-Rao Lower Bound (CRLB), 152 Classification Accuracy, 153 Cross-modulation, 150 Classification Systems, 121 CSI, 155 Cloud MIMO Radar, 112 Clustering, 155 Data Augmentation For Radar Data, 143 Clutter, 118 Data Conversion, 146 Clutter And Interference, 165 Data Fusion, 161 Clutter Cancelation, 114 Dataset, 124 Clutter Persistence, 164 Deep CNN, 143 Clutter Subspace Estimation, 118 Deep Learning, 116, 121, 127, 130, 132, 137, Clutter Suppression, 126 142, 143, 154 CNN, 143 Deep Reinforcement Learning, 120 Coexistance, 127 Deep/Machine Learning, 155 Coexistence, 107 Delay And Doppler Shift, 156 Cognitive, 162 Denoising, 127 Cognitive MIMO Radar, 131 Detection, 106 Cognitive Radar, 107, 133, 134, 139, 140, Detector Function, 110 149, 152, 157, 158, 170 Dictionary Learning, 129 Cognitive Rendezvous, 152 Digital, 163 Coherence, 168 Digital Array, 163, 164 Coherent Detection, 113 Digital Array Radar, 164 Coherent Integration, 105 Digital Arrays, 163 Coherent Jammer, 125 Digital Beam Forming (DBF), 147 Communication, 109, 150 Digital Beamforming, 147, 163, 164, 169 Compact Polarimetry, 147 Digital Filtering, 126 Complex Networks, 120 Digital Post-Distortion, 150 Complex Responses, 112 Digitally Modulated Radars, 115 Complex-valued Convolutional Neural Direct Data Domain, 148 Network, 127 Direct Prolate Spherical Sequence (DPSS), Complex-valued Convolutional Neural 119 Networks, 127 Direct RF, 126 Compound Gaussian Clutter, 113 Direction Of Arrival (DoA), 139 Compressed Sensing, 109 Direction Of Arrival Estimation, 129 Compressive Sensing, 106, 145, 156 Direction-Of-Arrival Estimations, 139 Computational Efficiency, 114 Discrete, 170 Consensus, 160 Discrete-Phase, 114 Constant Modulus Sequence Set, 115 Distributed, 162 Constant Modulus Waveform Design, 132 Distributed Detection, 162 Constellation, 147 Distributed GLRT-based Detection Of Target Continuous Matching Pursuit, 106 In SIRP Clutter And Noise, 112 Convex, 166 Distributed MIMO Radar, 132, 168 Convolutional Networks, 121 Distributed Optimization, 160 Convolutional Neural Network, 122, 141, 153, Distributed Radar, 136, 157, 169 154 Distributed Radar Imaging, 160, 161 Convolutional Neural Network (CNN), 153 Dithering, 108 Costas Array, 114 DOA Estimation, 138

181 IEEE RADARCONF 2021 KEYWORD INDEX

Doppler Effect, 145 FMCW, 109, 168, 171 Doppler Radar, 116, 137 FMCW Radar, 106, 108, 122–124, 171 Doppler Tolerance, 114 Forward-looking, 167 Downward-Looking, 172 Forward-Looking Radar, 160 DPD, 150 FPGA, 144, 146 Driving, 131 Frame-Level, 123 Drone, 122 Frequency, 132 Drone Detection, 122, 128 Frequency Diverse Array, 132, 138 Drone SAR, 124 Frequency Diverse Array (FDA), 139 DSP, 127, 146, 159 Frequency Diversity, 168 DSP DL BSL, 127 Frequency Division Multiplexing (FDM), 109 Duplexer, 126 Frequency Hopping, 114 DVB-S, 148 Frequency Invariant, 139 DVB-S Based Passive ISAR, 149 Frequency Multiplier, 124 DVB-S Signals, 129 Frequency-Agility, 113 DVB-T, 148 Fugl-Meyer Assessment, 138 Dynamic Hooking, 144 Fully Adaptive Radar, 158 Dynamic Range, 124, 126 Fusion, 158 Fusion Before Imaging, 171 Earth Science Missions, 146 Echolocation, 112 Gait Analysis, 130, 137 Educative, 123 GaN, 170 EHS, 170 GaoFen-3 Satellite, 125 Eigenjammer, 125 Gated Recurrent Units, 135 Electronic Attack, 125 Gaussian Cubature, 141 Electronic Intelligence, 141 Generalized Likelihood Ratio Test (GLRT), Electronic Protection, 125 162 Electronic Warfare, 125, 142 Generalized Wirtinger Flow (GWF), 149 Electronic Warfare (EW), 145 Generative Adversarial Networks, 134, 137 Electronics Cooling, 169 Geometric Deep Learning, 126 Element Level Digital Aperture, 164 Geometric Diversity, 168 Embedded, 142 Gesture Recognition, 123, 130, 131 Emission Prediction, 141 GGIW, 159 Entanglement, 110, 111, 117 Ghost Detection, 143 Entropy, 105 GLRT Detector, 113 Error-Correcting Output Codes, 142 GMTI, 118, 148 Estimation, 150 GPR, 171 Expectation Maximization, 156 GPU Framework, 119 Expectation-Maximization Algorithm, 152 GPU Kernel, 116 Gradient Methods, 140 Factor Graph, 159 Graph, 136 Factor Graphs, 151 Ground Moving Target Indication, 153 Feature Extraction, 123 Guard Bands, 150 Fields Of View, 159 Fighter Aircraft And Vehicle T, 125 Hankel Transform, 138 Fisher Information Matrix (FIM), 152 Harmonic Cancellation, 168 FISTA, 161 Harmonic Radar, 129 Five-domain-six-map (5D6M), 165 Harmonic Spur, 124

182 IEEE RADARCONF 2021 KEYWORD INDEX

Heading Angle, 116 kNN, 138 Heart Rate Monitoring, 137 Knowledge-Based Jammer, 125 Heterogeneous Clutter, 164 High Performance Computing, 165 Laminate, 170 High Range Resolution, 165 Large Clutter Discrete, 118 High Speed Targets Detection, 115 Layout Estimation, 153 High-Resolution Wide-Swath (HRWS) SAR, LFM, 108, 150 147 Likelihood Ratio, 110 High-Temperature Superconducting, 170 Likelihood Ratio Test (LRT), 112 Human Activity Classification, 127 Linear Quadratic, 152 Human Activity Recognition, 135, 136 Linearization, 157 Hybrid Regularization Method, 160 Long Short Term Memory, 122 Low Cost, 123 IBW, 126 Low Frequency RF-waves, 143 Image Formation, 115, 154 Low SNR, 160 Image Reconstruction, 121 LSTM Network, 128 Image Segmentation, 154 Imaging, 166 M-sequence, 115 IMFET, 170 Machine Learning, 118, 120, 135, 136, 138, Impedance Matching, 170 141–143, 154 Indoor Localisation, 143 Maritime, 111 Inhomogeneous Clutter, 134 Markov Chain, 141 InSAR, 146 Mars Exploration, 172 Integrate-Sidelobe-Level (ISL), 133 Matched Correlation, 108 Integrated Sidelobe Levels, 132 Matched Filter, 125 Intelligent, 162 Matched Illumination, 107 Interference, 108, 109 MaxSINR Beamforming, 131 Interference Alignment, 151 Message Passing, 151, 159 Interference Characterization, 134 Metacognition, 107, 134 Interference Mitigation, 109, 116, 127, 134 Micro-Doppler, 120 Interference Suppression, 151 micro-Doppler, 129, 130, 135, 137 Interferometric ISAR (InISAR), 159 Micro-Doppler Classification, 136 Interferometry, 147, 162 Micro-Doppler Effect, 143 Interpolation, 138 Micro-Doppler Signature, 122 Inverse Radon Transform, 128 micro-Doppler Signature, 122 Inverse Synthetic Aperture Radar, 156, 166 Micro-Doppler Signatures, 128 Inverse Synthetic Aperture Radar (ISAR), 159 micro-Dopplers, 137 ISAR, 160 Micro-motions, 122 Iterative Adaptive Approach, 119 Microwave Motion Sensor, 145 JCRS, 105 Microwave Photonics, 168 Joint Jamming Beam Selection And Power Millimeter Wave Radar, 131 Allocation (JJBSPA), 156 Millimeter-Wave, 124 Joint-Domain Processing, 114 Millimeter-wave Radar, 138, 158 JPDA, 130 MIMO, 106, 107, 163, 167, 168 MIMO Radar, 113, 132, 168 K-distribution, 113 mini-UAV SAR, 124 K-means Clustering, 118 Mismatched Filter, 114 Kalman Filter, 117 Mismatched Filtering, 107

183 IEEE RADARCONF 2021 KEYWORD INDEX

Mixed Reweighted L2, 131 Networking, 161 Mm-Wave Radar, 130 Networks, 162 Monopulse Radar Countermeasures, 145 Neural Network, 116, 122 Motion Parameter Estimation, 153 Neural Networks, 127, 136 Moving Platform, 148 NLEQ, 150, 163 Moving Target Classification, 143 NLFM, 108 Moving Target Imaging, 166 Noise Jammer, 125 Moving Target Indication, 114 Noise Radar, 107, 110 MPC, 153 Noise Square Root, 110 MTI, 114 Non-Contact, 145 Multi Domain Fusion, 136 Non-Contact Vital Signs, 131 Multi-Arm-Bandit, 133 Non-homogeneous Clutter, 111 Multi-hypothesis, 158 Non-Homogeneous Interference, 149 Multi-Input Multi-Output (MIMO) Radar, 109 Non-Orthogonal Waveforms, 132 Multi-Modal Learning, 129 Non-uniform Pulse Repetition Interval, 126 Multi-Objective Optimization, 168 Noncoherent Detection, 113 Multi-Target Tracking, 159 Nonidentical Pulses, 113 Multi-target Tracking, 158 Nonlinear Equalization, 150, 163 Multi-Targets Detection, 106 Nonlinear Memory, 150 Multiclass Classification, 142 Nonlinearity, 150 Multidimensional Imaging, 149 Numerical Integration, 141 Multiobject Tracking, 151 nuScenes, 126 Multipath, 151 Multipath Assisted, 153 Object Classification, 142 Multipath Mitigation, 108 Object Detection, 126 Multiple Extended Objects Tracking, 159 Object Recognition, 126 Multiple Heartbeat Detection, 130 Obstructed Line-Of-Sight, 151 Multiple Hypothesis Testing, 111 OFDM, 109 Multiple Signal Classification, 139 OFDM Radar, 115 Multiple-Input Multip, 151 OFDM-MIMO Radar, 109 Multiple-Input Multiple-Output (MIMO) Off-The-Grid, 106 Radar, 105, 133 One-Bit, 106 Multiple-Input Multiple-Output Radar, 115 Online Learning, 140 Multistatic, 162, 167 Open Source, 123 Multistatic Multichannel Passive Bistatic Open-Source, 124 Radar, 148 Optimal Transport, 155 Multistatic Radar, 149, 157, 161, 169 Optimization, 157 Multitarget Estimation, 156 Optimum Detecto, 113 Multitask Learning, 142 Orbital Angular Momentum, 150 MUSIC Algorithm, 172 OTH Radar, 165 Mutual Coupling Calibration, 164 Overconfidence, 142 Mutual Interference, 108, 134, 151 Overlapping Radar Signals, 136 Mutual Radio Frequency Interference, 125 PAE, 170 Navigation, 159 Parallel Block Improvement, 126 NCTR, 162 Parallel Implementing, 119 Netted Radar System, 156 Parallel-Computing, 116 Networked Radar, 157 Parameter Estimation, 112, 138

184 IEEE RADARCONF 2021 KEYWORD INDEX

Parametric Amplifier, 117 Quantum Target Ranging, 111 Particle Flow, 151 Particle Flow Filter, 111 Radar, 106, 109, 116, 120, 122, 125, 126, 129, Passive Coherent Location (PCL), 155 130, 134, 137, 142, 150, 157, Passive ISAR, 149 161–163, 165, 168 Passive Polarimetry, 149 Radar Autofocus, 161 Passive Radar, 107, 129, 148, 149, 155, 156 Radar Classification, 120 Passive WiFi Sensing, 137 Radar Clutter, 154 Pd, 150 Radar Communication, 109 Peak Sidelobe Level, 114 Radar Detection, 112, 134 Peak-to-Average Ratio (PAR), 133 Radar Emitter Identification, 141 Personnel Recognition, 128 Radar Imaging, 162, 166, 171, 172 Phase Array (PA), 151 Radar Machine Learning, 120 Phase Compensation, 105 Radar Measurements, 164 Phase Information, 128 Radar Network, 109 Phase Retrieval, 121 Radar Networks, 117, 133, 161, 169 Phase-Sensitive Amplification, 117 Radar Platform, 124 Phased Array, 163, 164 Radar Point Cloud Classification, 116 Phased Array Radar, 164 Radar Polarimetry, 146 PHD, 159 Radar Resource Allocation, 158 Physical Optics, 156 Radar Resource Management, 107, 157 Physical-layer Cyberattacks, 144 Radar Sensing, 128 Physics-Aware Machine Learning, 137 Radar Signal Processing, 116, 122, 135, 140 Planar Near-Field, 164 Radar Signals, 107 PMCW Radar, 115 Radar Simulation, 116 Point Scatterer Model, 165 Radar Testbed, 144 Polarimetric, 163 Radar Transmitters, 107 Polarization, 111 Radar-aided Positioning, 159 Position Estimation, 169 Radar-Communication, 151 Positioning, 153 Radar-Communication Convergence, 149 Power Amplifier (PA), 170 RADARSAT, 147 Power Amplifiers, 133, 140 Radio Frequency Interference Suppression, Primal–dual Type Algorithm, 114 141 Probabilistic Data Association, 151 Radio Spectrum Management, 133, 140 Probability Of Detection, 105, 156, 157 Radio Telescope LOFAR, 155 Projection Pursuits, 136 Random Activation, 115 Prototypical Networks, 130 Random Body Movement, 137 Pseudo-Polar Format, 167 Range Profile, 127 Pulse Diversity, 113 Range Resolution, 110 Pulse-On-Pulse Signal Separation, 136 Range-Doppler Ambiguity, 126 Range-Doppler Processing, 127 Q-learning, 157 Range-time Information, 128 QPSK, 150 Rank-1, 166 QTMS Radar, 110 Rao Test, 162 Quantization, 112 Raw Echo Signal, 167 Quantum Channel Discrimination, 111 RCM, 147 Quantum Noise, 110 RCS, 165 Quantum Radar, 110, 111, 117, 118 RCS Fluctuations, 105

185 IEEE RADARCONF 2021 KEYWORD INDEX

Real-Time Recognition, 135 Sequential Classification, 131 Receiver, 150 SER, 150 Receiver Operating Characteristic, 117 Shared Spectrum Access, 149 Receiver Operating Characteristics (ROC), Ship Classification, 153 112 Short Pulse, 165 Recurrent Neural Network, 141 Short-Range Surveillance, 129 Registration, 159 Short-time Fourier Transform, 135 Regularization, 143 Short-time Fourier Transform (STFT), 143 Reinforcement Learning, 127, 133, 157 Sidelobe, 153 RELAX, 167 Sign Language, 130 Remote Sensing, 130 Signal Decomposition, 135 Repeat Pass Radar Interferometry, 146 Signal Processing, 118, 134, 152 Repeater, 109 Signal-to-interference And Noise Ratio, 132 Resource Management, 120 Simulator, 137 Respiration Rate Monitoring, 137 Sinusoidal Surface, 165 Reverse Engineering, 144 Slepian Transform, 119 Revisit Interval Selection, 157 Small Caliber, 167 RF, 163 Software Defined Radar, 145 RF Convergence, 150 Software-Defined Radio (SDR), 143, 145 RF Sensing, 131 Soil Moisture Radar, 171 RFsensing, 130 Soli Radar, 144 RFSoC, 164 Space Debris, 155 RFView, 118 Space Surveillance, 105 Robust Beamforming, 119 Space-Time Adaptive Processing, 118 Robust Principal Component Analysis, 166 Spaceborne SAR, 147 ROC Curve, 110 Sparse Array, 138, 139 ROI Target, 167 Sparse Field, 167 Room Geometry, 153 Sparse Separation, 111 ROSE-L, 146 Sparse Step-Frequency Waveform, 109 Spectogram, 143 S-Band, 170 Spectral Efficiency, 150 SAR, 120, 121, 146, 153, 154, 159, 166, 167, Spectral Gap Extrapolation, 141 171 Spectrogram, 136 SAR Satellite, 147 Spectrum Management, 107 Satellite, 147 Spectrum Sharing, 107, 127, 133, 134, 140, Scan-To-Scan Integration, 113 150, 151 Scanning Radar, 139 STAP, 118, 119, 148 SCAT, 112 STAR, 126 Sea Clutter, 113 Staring Radar, 121 Search And Rescue, 129 Steering Invariant, 139 Second-Order Vertical Synchrosqueezing, Stepped-Frequency Quantum Radar, 110 135 Stepped-Impedance Cross Structure, 170 Self-Calibration, 164 Structural Similarity Index Measure, 145 Sensor Fusion, 117, 159 Subarea Performance, 168 Sensor Modeling, 116 Subarrays, 138 Sensor Processing And Architectures, 152 Subsurface Imaging, 145 Sentinel-1, 146 Sudoku, 114 Sentinel-1 Next Generation, 146 Sum-difference Channels, 138

186 IEEE RADARCONF 2021 KEYWORD INDEX

Sum-Product Algorithm, 151 Trigger Detection, 131 Super-Resolution, 160 Tunable Circuits And Devices, 170 Super-Resolution Imaging, 119 Two-Dimensional Group Sparsity, 131 Supervised Learning, 113 Two-Step Hybrid Algorithm, 167 Support Vector Machine, 142 SVM, 138 UAS Radar, 171 Synthetic Aperture Radar, 125, 146, 154, 166, UAV, 162 169 UAV Identification, 122 Synthetic Aperture Radar (SAR), 147, 153 Uavs, 121 Synthetic Jamming, 120 Ultra Wide Band Radar, 135 System, 168 Ultra-wideband, 165 Systems, 163 Ultra-Wideband Radar, 141 Uncertainty, 142 Target Detection, 111, 113, 117, 132, 134, Unmanned Aerial System, 171 140 Unmanned Aerial Vehicle, 128 Target Fluctuation Model, 105 UWB, 155 Target Reflectivity, 105 UWB Radar, 130 Target Resolution, 112 Variational Autoencoder, 137 Target Signature, 125 Velocity Ambiguity, 115 Target Tracking, 135, 158 Velocity Estimation, 116 TDM MIMO Radar, 115 Velocity Profile, 116 Template Matching, 139 Vertical Synchrosqueezing, 135 Temporal ISAR, 159 Virtual Array, 139 Terahertz Imaging, 123 Virtual Clutter, 118 Terahertz Radar, 123 Virtual Validation, 116 Threshold Optimization, 162 Visual Model, 153 Time Budget Management, 157 Vital Signs, 137 Time Delay, 112 Volterra Series, 150 Time Division Multiplexing (TDM), 109 Time Frequency Analysis, 136 WAA Fusion, 159 Time-Frequency Analysis, 128 Waterfilling, 150 Time-varying Random Air-Dielectric Waveform Agility, 114 Interface, 165 Waveform Design, 106, 114, 127, 150 Timing, 132 Waveform Diversity, 107 Toeplitz Matrix, 138 Waveform Generation, 107 Towed Jamming, 132 Waveform Notching, 107 Tracking, 152, 158 Weather Radar, 163 Tracking Radar, 145 Weight Fusion, 148 Trajectory, 167 Weighted Integrated Side Lobe Level, 115 Transfer Learning, 143 Wideband Transform, 119 Translational Motion Compensation (TMC), Wiener Process, 110 160 WiFi, 143, 155 Transmit Waveform Shaped Jammer, 125 Wigner Function, 111 Triangular Waveform, 106 Wireless Sensor Network, 162

187