Adaptive Motion Compensation in Sonar Array Processing

Total Page:16

File Type:pdf, Size:1020Kb

Adaptive Motion Compensation in Sonar Array Processing Adaptive motion compensation in sonar array processing Adaptive motion compensation in sonar array processing Proefschrift ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof.dr.ir. J.T. Fokkema, voorzitter van het College voor Promoties, in het openbaar te verdedigen op vrijdag 2 juni 2006 om 10:00 uur, door Johannes GROEN wiskundig ingenieur geboren te Den Haag Dit proefschrift is goedgekeurd door de promotoren: Prof. dr. ir. A. Gisolf Prof. dr. D.G. Simons Samenstelling promotiecommissie: Rector Magnificus, voorzitter Prof. dr. ir. A. Gisolf, Technische Universiteit Delft, promotor Prof. dr. D.G. Simons, Technische Universiteit Delft, promotor Prof. ir. P. van Genderen Technische Universiteit Delft Prof. dr. M.E. Zakharia Ecole Navale, Brest, Frankrijk Prof. dr. A. Stepnowski Gdansk University of Technology, Polen Dr. ir. G. Blacquière, Nederlandse organisatie voor Toegepast Natuurwetenschappelijk Onderzoek Dr. S.P. Beerens, Nederlandse organisatie voor Toegepast Natuurwetenschappelijk Onderzoek Published and distributed by: Netherlands organization for applied scientific research (TNO) P.O. Box 96864 2509 JG The Hague The Netherlands Telephone: +31 70 374 00 00 Telefax: +31 70 374 06 54 E-mail: [email protected] ISBN-10: 90-5986-187-6 ISBN-13: 978-90-5986-187-6 Copyright 2006 by J. Groen All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without written permission from the author: J. Groen, TNO, P.O. Box 96864, 2509 JG, The Hague, The Netherlands. Printed in The Netherlands 5 Contents 1. Introduction.......................................................................................................................9 1.1. Background ...............................................................................................................10 1.2. Objective...................................................................................................................11 1.3. Anti Submarine Warfare...........................................................................................11 1.4. Mine hunting.............................................................................................................13 1.5. Requirements analysis ..............................................................................................15 1.5.1. Subdivision in four topics .................................................................................15 1.5.2. Problems and approach.....................................................................................18 1.5.3. Research visualization ......................................................................................18 1.6. Outline.......................................................................................................................19 2. Simulation of sonar data and images ............................................................................21 2.1. Introduction...............................................................................................................21 2.1.1. Basic physics for the simulator suite ................................................................21 2.1.2. Simulator structure............................................................................................25 2.1.3. Primary assumptions in the model....................................................................25 2.1.4. Sonar background knowledge and definitions..................................................25 2.1.5. Outline...............................................................................................................29 2.2. Application of the simulator .....................................................................................29 2.2.1. Application to mine hunting sonar....................................................................30 2.2.2. Application to Anti-Submarine Warfare (ASW) sonars...................................31 2.3. Structure of the simulator..........................................................................................33 2.4. Modular methodology with a collection of models..................................................34 2.4.1. Acoustic response of arbitrary targets...............................................................34 2.4.2. Reverberation....................................................................................................43 2.4.3. Target shadow...................................................................................................48 2.4.4. Multi-path .........................................................................................................49 2.5. Complete simulation for mine hunting .....................................................................52 2.6. Measured synthetic aperture sonar data....................................................................54 2.6.1. Use for signal processing development ............................................................55 2.6.2. Potential for classification.................................................................................59 2.7. Specific features for the Anti-Submarine Warfare towed array sonars ....................59 2.8. Summary and conclusion..........................................................................................62 6 3. Adaptive port/starboard beamforming of triplet sonar arrays..................................63 3.1. Introduction...............................................................................................................63 3.2. Triplet beamformers..................................................................................................66 3.2.1. Optimum triplet beamformer ............................................................................70 3.2.2. Cardioid beamformer........................................................................................71 3.2.3. Adaptive triplet beamformer.............................................................................73 3.3. Theoretical performance ...........................................................................................76 3.4. Sea trial results in noise ............................................................................................80 3.4.1. Experiment set-up .............................................................................................80 3.4.2. Processing chain................................................................................................81 3.4.3. Experiment results ............................................................................................82 3.4.4. Discussion.........................................................................................................84 3.5. Sea trial results in reverberation ...............................................................................84 3.6. Conclusion ................................................................................................................89 4. Doppler corrected array processing..............................................................................91 4.1. Introduction...............................................................................................................91 4.1.1. Background .......................................................................................................91 4.1.2. Operational aspects...........................................................................................93 4.1.3. Outline...............................................................................................................94 4.2. Theoretical performance in a turn.............................................................................94 4.2.1. Error sources.....................................................................................................95 4.2.2. Shape deviations ...............................................................................................95 4.2.3. Doppler shift .....................................................................................................98 4.2.4. Doppler spreading...........................................................................................101 4.3. Compensation methods...........................................................................................103 4.3.1. Signal processing chain...................................................................................103 4.3.2. Method 1: Shape corrected beamforming.......................................................105 4.3.3. Method 2: Stabilized beamforming and dynamic matched filtering ..............106 4.3.4. Method 3: Shape and Doppler corrected beamforming (SDCB)....................107 4.4. Required sonar track accuracy................................................................................108 4.5. Proof of concept on simulations .............................................................................110 4.6. North Sea experiments in 2002...............................................................................111 4.6.1. Experiment description...................................................................................112 4.6.2. Environmental conditions ...............................................................................112 4.6.3. Signal processing
Recommended publications
  • Coarrays, MUSIC, and the Cramér Rao Bound
    1 Coarrays, MUSIC, and the Cramer´ Rao Bound Mianzhi Wang, Student Member, IEEE, and Arye Nehorai, Life Fellow, IEEE Abstract—Sparse linear arrays, such as co-prime arrays and ESPRIT [17]) via first-order perturbation analysis. However, nested arrays, have the attractive capability of providing en- these results are based on the physical array model and hanced degrees of freedom. By exploiting the coarray structure, make use of the statistical properties of the original sample an augmented sample covariance matrix can be constructed and MUSIC (MUtiple SIgnal Classification) can be applied to covariance matrix, which cannot be applied when the coarray identify more sources than the number of sensors. While such model is utilized. In [18], Gorokhov et al. first derived a a MUSIC algorithm works quite well, its performance has not general MSE expression for the MUSIC algorithm applied been theoretically analyzed. In this paper, we derive a simplified to matrix-valued transforms of the sample covariance matrix. asymptotic mean square error (MSE) expression for the MUSIC While this expression is applicable to coarray-based MUSIC, algorithm applied to the coarray model, which is applicable even if the source number exceeds the sensor number. We show that its explicit form is rather complicated, making it difficult to the directly augmented sample covariance matrix and the spatial conduct analytical performance studies. Therefore, a simpler smoothed sample covariance matrix yield the same asymptotic and more revealing MSE expression is desired. MSE for MUSIC. We also show that when there are more sources In this paper, we first review the coarray signal model than the number of sensors, the MSE converges to a positive commonly used for sparse linear arrays.
    [Show full text]
  • The 10Th EAA International Symposium on Hydroacoustics Jastrzębia Góra, Poland, May 17 – 20, 2016
    ARCHIVES OF ACOUSTICS Copyright c 2016 by PAN – IPPT Vol. 41, No. 2, pp. 355–373 (2016) DOI: 10.1515/aoa-2016-0038 The 10th EAA International Symposium on Hydroacoustics Jastrzębia Góra, Poland, May 17 – 20, 2016 The 10th EAA International Symposium on Hy- Dr. Christopher Jenkins: Backscatter from In- droacoustics, which is also the 33rd Symposium on • tensely Biological Seabeds – Benthos Simulation Hydroacoustics in memory of Prof. Leif Børnø orga- Approaches; nized in Poland, will take place from May 17 to 20, Prof. Eugeniusz Kozaczka: Technical Support for 2016, in Jastrzębia Góra. It will be a forum for re- • National Border Protection on Vistula Lagoon and searchers, who are developing hydroacoustics and re- Vistula Spit; lated issues. The Symposium is organized by the Prof. Andrzej Nowicki et al.: Estimation of Ra- Gdańsk University of Technology and the Polish Naval • dial Artery Reactive Response using 20 MHz Ul- Academy. trasound; The Scientific Committee comprises of the world – Prof. Jerzy Wiciak: Advances in Structural Noise class experts in this field, coming from, among others, • Germany, UK, USA, Taiwan, Norway, Greece, Russia, Reduction in Fluid. Turkey and Poland. The chairman of Scientific Com- All accepted papers will be published in the periodical mittee is Prof. Eugeniusz Kozaczka, who is the Pres- “Hydroacoustics”. ident of Committee on Acoustics Polish Academy of Sciences and Chairman of Technical Committee Hy- droacoustics of European Acoustics Association. Abstracts The Symposium will include invited lectures, struc-
    [Show full text]
  • Acoustic Seabed and Target Classification Using Fractional
    University of New Orleans ScholarWorks@UNO University of New Orleans Theses and Dissertations Dissertations and Theses 12-15-2006 Acoustic Seabed and Target Classification using rF actional Fourier Transform and Time-Frequency Transform Techniques Madalina Barbu University of New Orleans Follow this and additional works at: https://scholarworks.uno.edu/td Recommended Citation Barbu, Madalina, "Acoustic Seabed and Target Classification using rF actional Fourier Transform and Time-Frequency Transform Techniques" (2006). University of New Orleans Theses and Dissertations. 480. https://scholarworks.uno.edu/td/480 This Dissertation is protected by copyright and/or related rights. It has been brought to you by ScholarWorks@UNO with permission from the rights-holder(s). You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Dissertation has been accepted for inclusion in University of New Orleans Theses and Dissertations by an authorized administrator of ScholarWorks@UNO. For more information, please contact [email protected]. Acoustic Seabed and Target Classication using Fractional Fourier Transform and Time-Frequency Transform Techniques A Dissertation Submitted to the Graduate Faculty of the University of New Orleans in partial fulllment of the requirements for the degree of Doctor of Philosophy in Engineering and Applied Sciences by Madalina Barbu B.S./MS, Physics, University of Bucharest, Romania, 1993 MS, Electrical Engineering, University of New Orleans, 2001 December, 2006 c 2006, Madalina Barbu ii To my family iii Acknowledgments I would like to express my appreciation to Dr.
    [Show full text]
  • Fixed Sonar Systems the History and Future of The
    THE SUBMARINE REVIEW FIXED SONAR SYSTEMS THE HISTORY AND FUTURE OF THE UNDEWATER SILENT SENTINEL by LT John Howard, United States Navy Naval Postgraduate School, Monterey, California Undersea Warfare Department Executive Summary One of the most challenging aspects of Anti-Submarine War- fare (ASW) has been the detection and tracking of submerged contacts. One of the most successful means of achieving this goal was the Sound Surveillance System (SOSUS) developed by the United States Navy in the early 1950's. It was designed using breakthrough discoveries of the propagation paths of sound through water and intended to monitor the growing submarine threat of the Soviet Union. SOSUS provided cueing of transiting Soviet submarines to allow for optimal positioning of U.S. ASW forces for tracking and prosecution of these underwater threats. SOSUS took on an even greater national security role with the advent of submarine launched ballistic missiles, ensuring that U.S. forces were aware of these strategic liabilities in case hostilities were ever to erupt between the two superpowers. With the end of the Cold War, SOSUS has undergone a number of changes in its utilization, but is finding itself no less relevant as an asset against the growing number of modern quiet submarines proliferating around the world. Introduction For millennia, humans seeking to better defend themselves have set up observation posts along the ingress routes to their key strongholds. This could consist of something as simple as a person hidden in a tree, to extensive networks of towers communicating 1 APRIL 2011 THE SUBMARINE REVIEW with signal fires.
    [Show full text]
  • Iterative Sparse Asymptotic Minimum Variance Based Approaches For
    1 Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing Habti Abeida∗, Qilin Zhangy, Jian Liz and Nadjim Merabtinex ∗Department of Electrical Engineering, University of Taif, Al-Haweiah, Saudi Arabia yDepartment of Computer Science, Stevens Institue of Technology, Hoboken, NJ, USA zDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA xDepartment of Electrical Engineering, University of Taif, Al-Haweiah, Saudi Arabia Abstract This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose a series of power-based iterative SAMV approaches, which are robust against insufficient snapshots, coherent sources and arbitrary array geometries. Moreover, to over- come the direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML (SAMV-SML) approaches are derived by explicitly minimizing a closed form stochastic ML cost function with respect to one scalar parameter, eliminating the need of any additional grid refinement techniques. To assist the performance evaluation, approximate solutions to the SAMV approaches are also provided at high signal-to-noise ratio (SNR) and low SNR, respectively. Finally, numerical examples are generated to compare the performance of the proposed approaches with existing approaches. Index terms: Array Processing, AMV estimator, Direction-Of-Arrival (DOA) estimation, Sparse parameter estima- tion, Covariance matrix, Iterative methods, Vectors, Arrays, Maximum likelihood estimation, Signal to noise ratio, SAMV approach.
    [Show full text]
  • MARITIME Security &Defence M
    June MARITIME 2021 a7.50 Security D 14974 E &Defence MSD From the Sea and Beyond ISSN 1617-7983 • Key Developments in... • Amphibious Warfare www.maritime-security-defence.com • • Asia‘s Power Balance MITTLER • European Submarines June 2021 • Port Security REPORT NAVAL GROUP DESIGNS, BUILDS AND MAINTAINS SUBMARINES AND SURFACE SHIPS ALL AROUND THE WORLD. Leveraging this unique expertise and our proven track-record in international cooperation, we are ready to build and foster partnerships with navies, industry and knowledge partners. Sovereignty, Innovation, Operational excellence : our common future will be made of challenges, passion & engagement. POWER AT SEA WWW.NAVAL-GROUP.COM - Design : Seenk Naval Group - Crédit photo : ©Naval Group, ©Marine Nationale, © Ewan Lebourdais NAVAL_GROUP_AP_2020_dual-GB_210x297.indd 1 28/05/2021 11:49 Editorial Hard Choices in the New Cold War Era The last decade has seen many of the foundations on which post-Cold War navies were constructed start to become eroded. The victory of the United States and its Western Allies in the unfought war with the Soviet Union heralded a new era in which navies could forsake many of the demands of Photo: author preparing for high intensity warfare. Helping to ensure the security of the maritime shipping networks that continue to dominate global trade and the vast resources of emerging EEZs from asymmetric challenges arguably became many navies’ primary raison d’être. Fleets became focused on collabora- tive global stabilisation far from home and structured their assets accordingly. Perhaps the most extreme example of this trend has been the German Navy’s F125 BADEN-WÜRTTEMBERG class frig- ates – hugely sophisticated and expensive ships designed to prevail only in lower threat environments.
    [Show full text]
  • Matteo Bernasconi Phd Thesis
    THE USE OF ACTIVE SONAR TO STUDY CETACEANS Matteo Bernasconi A Thesis Submitted for the Degree of PhD at the University of St Andrews 2012 Full metadata for this item is available in Research@StAndrews:FullText at: http://research-repository.st-andrews.ac.uk/ Please use this identifier to cite or link to this item: http://hdl.handle.net/10023/2580 This item is protected by original copyright This item is licensed under a Creative Commons Licence The use of active sonar to study cetaceans Matteo Bernasconi Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy University of St Andrews July 2011 The use of active sonar to study cetaceans Matteo Bernasconi TABLE OF CONTENTS DECLARATIONS V ACKNOWLEDGMENTS VII ABSTRACT IX 1. INTRODUCTION 1 2. UNDERWATER ACTIVE ACOUSTIC 13 2.1 Historical notes 15 2.2 Sound: basic concepts 17 2.2.1 Sound propagation 18 2.2.2 Sound pressure and intensity 20 2.2.3 The decibel 21 2.2.4 Transmission Loss 22 2.2.5 Sound Speed 25 2.3 Transducers and beams 26 2.3.1 The beam pattern 28 2.3.2 The equivalent beam angle 29 2.3.3 Pulse and Ranging 30 2.4 Acoustic scattering 31 2.4.1 Target Strength 32 2.4.2 Target shape and orientation 33 2.4.3 Volume/area scattering coefficient 34 2.5 The sonar equation 35 3. CALIBRATION 39 3.1 The on‐axis sensitivity 41 3.2 Nearfield and Farfield 42 3.3 The TVG function 43 3.4 Standard experimental procedure 44 3.5 Calibration spheres 46 3.6 Calibration test of omnidirectional Sonar 47 3.6.1 Introduction 48 3.6.2 Method 49 3.6.3 Results & Discussion 51 3.6.4 Conclusion 57 4.
    [Show full text]
  • Dynamic Towed Array Models and State Estimation for Underwater Target Tracking
    Calhoun: The NPS Institutional Archive Theses and Dissertations Thesis Collection 2013-09 Dynamic towed array models and state estimation for underwater target tracking Stiles, Zachariah H. Monterey, California: Naval Postgraduate School http://hdl.handle.net/10945/37725 NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS DYNAMIC TOWED ARRAY MODELS AND STATE ESTIMATION FOR UNDERWATER TARGET TRACKING by Zachariah H. Stiles September 2013 Thesis Co-Advisors: Robert G. Hutchins Xiaoping Yun Approved for public release; distribution is unlimited THIS PAGE INTENTIONALLY LEFT BLANK REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED September 2013 Master’s Thesis 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS DYNAMIC TOWED ARRAY MODELS AND STATE ESTIMATION FOR UNDERWATER TARGET TRACKING N/A 6. AUTHOR(S) Zachariah H. Stiles 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION Naval Postgraduate School REPORT NUMBER Monterey, CA 93943-5000 9. SPONSORING /MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10.
    [Show full text]
  • A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition
    electronics Review A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition Dhiraj Neupane and Jongwon Seok * Department of Information and Communication Engineering, Changwon National University, Changwon-si, Gyeongsangnam-do 51140, Korea; [email protected] * Correspondence: [email protected] Received: 13 October 2020; Accepted: 19 November 2020; Published: 22 November 2020 Abstract: Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in
    [Show full text]
  • Evaluation of MUSIC Algorithm for DOA Estimation in Smart Antenna
    IARJSET ISSN (Online) 2393-8021 ISSN (Print) 2394-1588 International Advanced Research Journal in Science, Engineering and Technology Vol. 3, Issue 5, May 2016 Evaluation of MUSIC algorithm for DOA estimation in Smart antenna Banuprakash.R1, H.Ganapathy Hebbar2, Sowmya.M3, Swetha.M4 Assistant Professor, Department of TE, BMS Institute of Technology and Management, Bengaluru, India1,2 Final Year Students, Department of TE, BMS Institute of Technology and Management, Bengaluru, India3,4 Abstract: This paper presents a simulation study of a smart antenna system on basis of direction-of-arrival estimation for identifying the direction of desired signal using Multiple Signal Classification (MUSIC) algorithm. In recent times smart antennas are gaining more popularity by increasing user capacity, achieved by effectively reducing interference. In this paper, performance and resolution analysis of the algorithm is made. Its sensitivity to various perturbations and the effect of parameters such as the number of array elements and the spacing between them which are related to the design of the array sensor are investigated. The analysis is based on uniform linear array antenna and the calculation of the pseudo spectrum function of the algorithm. MATLAB is used for simulating the algorithm. Keywords: Smart antenna, DOA, MUSIC, Pseudo spectrum, MATLAB. I. INTRODUCTION 1.1 DOA ESTIMATION ALGORITHM DOA is a research area in array signal processing and The requirement of mobile communication resources has many engineering application such as radar, radio been increased rapidly over few years. The smart astronomy, sonar and other emergency assistance devices techniques or adaptive beamforming techniques have been that need to be supported by estimation of DOA[2].
    [Show full text]
  • Antenna Array Processing: Autocalibration and Fast High-Resolution Methods for Automotive Radar
    Antenna Array Processing: Autocalibration and Fast High-Resolution Methods for Automotive Radar Vom Fachbereich 18 Elektrotechnik und Informationstechnik der Technischen Universit¨at Darmstadt zur Erlangung der W¨urde eines Doktor-Ingenieurs (Dr.-Ing.) genehmigte Dissertation von Dipl.-Ing. Philipp Heidenreich geboren am 27.12.1980 in R¨usselsheim Referent: Prof. Dr.-Ing. Abdelhak M. Zoubir Korreferent: Prof. Dr.-Ing. Bin Yang Tag der Einreichung: 29.02.2012 Tag der m¨undlichen Pr¨ufung: 06.06.2012 D 17 Darmstadt, 2012 I Danksagung Die vorliegende Arbeit entstand im Rahmen meiner T¨atigkeit als wissenschaftlicher Mitarbeiter am Fachgebiet Signalverarbeitung des Instituts f¨ur Nachrichtentechnik der Technischen Universit¨at Darmstadt. An dieser Stelle gilt mein Dank allen, die das Entstehen der Arbeit direkt oder indirekt erm¨oglicht haben. Besonders m¨ochte ich mich bei Prof. Dr.-Ing. Abdelhak Zoubir f¨ur die wissenschaftliche Betreuung der Arbeit bedanken. Seine stete Bereitschaft zur Diskussion und die F¨orderung von Publikationen und Projekten, sowie das Erm¨oglichen von Konferenz- reisen, haben maßgeblich zum Gelingen der Arbeit beigetragen. Des Weiteren bedanke ich mich bei Prof. Dr.-Ing Bin Yang f¨ur die freundliche Ubernahme¨ des Korreferats und sein Interesse an meiner Arbeit. Ebenso gilt mein Dank Prof. Dr.-Ing. Klaus Hofmann, Prof. Dr.-Ing. Rolf Jakoby und Prof. Dr.-Ing. habil. Tran Quoc Khanh f¨ur ihre Mitwirkung in der Pr¨ufungskommission. F¨ur die Kooperation und F¨orderung meiner Promotion m¨ochte ich mich bei der A.D.C. GmbH der Continental AG bedanken. Mein besonderer Dank gilt Florian Engels, Dr. Alexander Kaps, Dr. Peter Seydel und Dr.
    [Show full text]
  • Department of the Navy SBIR/STTR Success Stories
    Department of the Navy SBIR/STTR Success Stories Small Business Innovation Research/Small Business Technology Transfer T hanks to all the companies for their participation in this Navy SBIR/STTR Success Story publication. We appreciate the time and effort it took to compile and share facts, details, and graphics for the stories. For more information about this publication or additional copies, please contact: Office of Naval Research ONR 364, SBIR/STTR Program 800 North Quincy Street Arlington, VA 22217-5660 www.onr.navy.mil/sbir Department of the Navy SBIR/STTR Success Stories Small Business Innovation Research/Small Business Technology Transfer I Department of the Navy SBIR/STTR Program II Dedicated to VINCENT D. SCHAPER for his exemplary contribution and service to the Navy and to American Small Businesses as the Navy SBIR Program Manager from 1988 to 2004. All of your associates, colleagues and friends thank you, and wish you well in your retirement. III Department of the Navy SBIR/STTR Program IV Letter from the Admiral Foreword US small businesses provide innovative ideas and create many of the new technologies which drive the capabilities the Navy is seeking to maintain and modernize the naval fleet. The Navy's SBIR/STTR program is primarily a mission oriented program which affords companies the opportunity to become part of the national technology base that can feed both the military and private sectors of the nation. The goal is to transition the small business research into active naval systems. This is extremely difficult to accomplish. On the government side, priorities change, program funding evaporates, champions leave and funding for critical demonstrations may be hard to obtain.
    [Show full text]