Year Four Annual Report

U.S. Department of Homeland Security National Center of Excellence Center for Secure and Resilient Maritime Commerce (CSR)

YEAR FOUR REPORT

September 2012 Cooperative Agreement 2008-ST-061-ML0001 Department of Homeland Security

U.S. Department of Homeland Security National Center of Excellence Center for Secure and Resilient Maritime Commerce (CSR)

DHS Leadership

Dr. Matthew Clark, Director, University Programs, Science and Technology Directorate Mr. Theophilos C. Gemelas, Program Manager, University Programs

Team Participants

University Partners

Massachusetts Institute of Technology Monmouth University Rutgers University Stevens Institute of Technology (lead institution) University of Miami University of Puerto Rico at Mayaguez

Non-University Partners

Mattingley Group Nansen Environmental Remote Sensing Center Pacific Basin Development Council Port Authority of New York and New Jersey

2 TABLE OF CONTENTS

Executive Summary …………………………… 4

I. Introduction …………………………… 8

II. Description of the CSR …………………………… 15 Ø CSR Team Members …………………………… 15 Ø Advisory Board …………………………… 16 Ø Cooperating Programs …………………………… 16 Ø Collaboration with co-lead CIMES …………………………… 18 Ø Communications and outreach …………………………… 19 Ø Collaborations with other COEs …………………………… 19

III. Research Project Reports …………………………… 20 Ø Description of Research Areas …………………………… 20 Ø Task 1.1 Space-Based Wide Area Surveillance …………………… 21 Ø Task 1.2 Investigation of HF Radar for Multiple Applications: Over-The-Horizon Vessel Detection and Tracking, Search and Rescue, and Environmental Monitoring …………………………… 35 Ø Task 1.3 Nearshore and Harbor Maritime Domain Awareness Via Layered Technologies …………………………… 59 Ø Task 1.4 Decision-Support Systems …………………………… 114 Ø Task 2.1 Resiliency Strategies for Port Systems …………………… 124 Ø Task 2.3 Port Resilience Project …………………………… 126

IV. Educational Activities …………………………… 136

3 YEAR FOUR EXECUTIVE SUMMARY

The Center for Secure and Resilient Maritime Commerce (CSR), along with the University of Hawaii’s National Center for Islands, Maritime, and Extreme Environments Security (CIMES), are the U.S. Department of Homeland Security’s (DHS) National Center of Excellence for Maritime, Island and Extreme/Remote Environment Security (MIREES). The Center supports DHS efforts under NSPD-41 / HSPD-13 to provide for the safe and secure use of our nation’s maritime domain (including island and extreme environments and inland waterways), and a resilient MTS, through advancement of the relevant sciences and development of the new workforce.

The CSR brings together a unique group of academic institutions and public and private partners that is led by Stevens Institute of Technology, Hoboken, New Jersey. Besides Stevens Institute, the partnership includes the following academic institutions: Rutgers University, University of Miami, University of Puerto Rico, Massachusetts Institute of Technology, Monmouth University and the U.S. Merchant Marine Academy’s Global Maritime and Transportation School. The non-university partners in the CSR include the Port Authority of New York and New Jersey, the Mattingley Group, the Pacific Basin Development Council, and Nansen Environmental Remote Sensing Center.

The CSR strategy to succeed in its mission relies on the creation and sustainment of a truly collaborative research and education enterprise that draws on the discipline-specific strengths of each partner, their intellectual and physical infrastructure assets, and their leveraged relevant DHS and non-DHS research and education activities. The Center, now in its 5th year of operation, possesses extraordinarily diverse expertise and significant experience in developing new knowledge, models, tools, policies and procedures, and education/training methodologies related to maritime security and safety.

In order to ensure alignment of the CSR research and education activities with the implementation plans under NSPD-41/HSPD-13, while also ensuring both the relevance and agility needed to support the DHS member agencies, we have developed a set of metrics that serve both to guide the Center’s activities and to facilitate the performance assessment of those activities. Fundamentally, these activities center on supporting the safety and security of our nation’s Marine Transportation System (MTS) and coastal and offshore resources. To address these goals, CSR’s research and education efforts are divided into two basic areas: Maritime Domain Awareness (MDA), and Topics in Global Policies influencing MTS Security, including Design for Resiliency.

Maritime Domain Awareness: The Maritime Domain is defined as "all areas and things of, on, under, relating to, adjacent to, or bordering on a sea, ocean, or other navigable waterway, including all maritime-related activities, infrastructure, people, cargo, and vessels and other conveyances.” The MDA projects examine the basic science issues and emerging technologies to support the use of a layered approach to the problem. The layers include satellite-based wide area surveillance; HF Radar systems providing over- the horizon surveillance; and nearshore and harbor surveillance systems centered on underwater acoustic technologies. Integration of these systems is aimed at achieving

4 vessel detection, classification, identification, and tracking. The new knowledge and new and improved technologies and algorithms developed under this research area are aimed at achieving real-time, all-weather, day/night, multi-layer maritime surveillance from the open ocean to estuaries, harbors and inland waterways, all at high-resolution. In year 4 we continued and expanded our partnerships with key stakeholders. We conducted targeted projects with DHS stakeholders (CBP) and demonstrated the ways our technologies could be used singly and in conjunction with each other to enhance MDA in operational settings.

The University of Miami’s Center for Southeastern Tropical Advanced Remote Sensing (CSTARS) leads the space-base applications and is developing new understanding and new processes for receiving and analyzing large maritime area data from multi-satellite and multi-frequency sensors such as Synthetic Aperture Radar (SAR) and electro-optical (EO) sensors. Algorithms continue to be developed to employ the data to detect vessels, including small ships, in harbors, inland waterways, the coastal ocean and the high seas. Algorithms are also being developed to integrate this vessel detection information with ground-based systems such as Automatic Identification System (AIS). Key achievements in year 4 include the supply of satellite images and analysis to USCG to meet operational needs. Progress on the EDGE interface to task satellite retrievals, including a mobile capability, facilitates the E2E work to be undertaken in year 5.

Rutgers University’s High-Frequency Surface Wave Radar (HF Radar) team is developing robust detection algorithms that recognize ship-associated HF Radar signals above the background noise (e.g., surface waves). Algorithms continue to be developed to support vessel detection and tracking capabilities using compact HF Radars, demonstrating that ships, including small ships, can be detected and tracked by multistatic HF Radar in a multi-ship environment, while simultaneously mapping ocean currents. This has been demonstrated for Puerto Rico in year 4 (in collaboration with UPRM), and NY/NJ harbor in year 3 - with real-time detection attained in year 4 for NY/NJ harbor.

Stevens Institute of Technology leads the near-shore and harbor surveillance system portion of the MDA project. Much of the Stevens effort has been devoted to the development of a passive acoustic array that can provide low-cost, highly portable acoustic surveillance capability. The signal processing is based on the cross-correlation of signals received by several hydrophones. The system has been applied to measuring the acoustic signature characteristics of vessels in the heavy traffic of NY Harbor for a long- term deployment, as well as in the San Diego area for a DHS S&T project focused on gaining actionable information in an operational setting. A patent was awarded to the passive acoustics system this year (Bruno et al., Patent Number 8,195,409 awarded 5 June 2012).

Stevens researchers are also conducting investigations of emergency response decision- making, and have published several seminal papers in this area, as well as receiving a patent for “Detection of Hostile Intent from Movement Patterns.” (Nickerson et al., Patent Number 8,145,591 awarded 27 March 2012)

MTS Resiliency: The MTS Resiliency work is aimed at developing a more detailed MTS capacity study. Year 4 efforts included surveys at ports along the inland waterways and the synthesis of the findings. Also accomplished in year 4 was a novel web-based application called the Port Mapper to address key questions such as: - Where could cargo move to if there were a disruption at a major US port? - What other ports handle the same cargo types as the disrupted port? - How far away are those ports? - Does the US system of ports have enough capacity to handle a disruption to a major US port? - How much additional port capacity is necessary for the US ports to handle a disruption and avoid significant delays and costs to the US economy? The Port Mapper has been shown to stakeholders at DHS, USCG, MARAD, NMIO, among others, and is being further enhanced to meet the needs of end-users.

This work aims to provide the modeling framework, visualization tools, and data that will enable an assessment of the present state of the U.S. MTS in terms of vulnerability, capacity, and ultimately, resilience, as well as the preliminary tools necessary for an eventual capability to design for resilience.

Education Activities: Central to CSR’s mission is the transfer of its research and expertise into highly relevant, innovative educational programs designed to enhance maritime domain awareness and the knowledge, technical skills and leadership capabilities of our nation’s current and future maritime security workforce. Since the Center’s inception in 2008, CSR, via collaboration with its academic partners, Stevens Institute of Technology, Rutgers University, University of Miami, University of Puerto Rico, Massachusetts Institute of Technology and Monmouth University, have worked together to develop a comprehensive portfolio of maritime security-centric educational programs. These programs include: • Curriculum development in MDA for undergraduate education, • Professional development courses in port security sensing technologies tailored to maritime industry and homeland security practitioners, and • A four-course Graduate Certificate in Maritime Security delivered online via Stevens Institute of Technology’s WebCampus.

In June 2012, CSR conducted the third installment of its primary educational initiative - the Summer Research Institute - a multi-disciplinary, intensive summer research program designed to provide qualified undergraduate and graduate-level students with the unique opportunity to engage in rigorous hands-on research in collaboration with CSR faculty to address critical issues in MDA, the MTS, emergency response and preparedness, and maritime system resilience. The themes of this year’s Summer Research Institute were “Transitioning Technologies” and “End-User Engagement.” Speakers and site visits highlighted these themes.

In addition, CSR continues to be highly successful at receiving competitively awarded

6 DHS Career Development Maritime Systems Fellowship Awards for masters degrees.

7 I. INTRODUCTION

Mission The National Center of Excellence for Maritime, Island & Remote/Extreme Environment Security supports DHS efforts under NSPD-41 / HSPD-13 (National Maritime Security Strategy, 21 December, 2004) to provide for the safe and secure use of our nation’s maritime domain (including island and extreme environments, and inland waterways), and a resilient Marine Transportation System (MTS), through advancement of the relevant sciences and development of the new workforce. These efforts are specifically aligned to assist in the successful implementation of the elements of NSPD-41 / HSPD- 13: • Maritime Domain Awareness - the effective understanding of anything associated with the Maritime Domain that could impact the security, safety, economy, or environment of the United States. • Global Maritime Intelligence Integration - uses existing capabilities to integrate all available intelligence regarding potential threats to U.S. interests in the Maritime Domain. • Maritime Operational Threat Response - aims for coordinated U.S. Government response to threats against the US and its interests in the Maritime Domain by establishing roles and responsibilities. • International Outreach and Coordination - provides a framework to coordinate all maritime security initiatives undertaken with foreign governments and international organizations, and solicits international support for enhanced maritime security. • Maritime Infrastructure Recovery - recommends procedures and standards for the recovery of the maritime infrastructure following attack or similar disruption. • MTS Security - responds to the President’s call for recommendations to improve the national and international regulatory framework regarding the maritime domain. • Maritime Commerce Security - establishes a comprehensive plan to secure the maritime supply chain. • Domestic Outreach - engages non-Federal input to assist with the development and implementation of maritime security policies resulting from NSPD-41/HSPD-13.

The Maritime Domain is defined as "All areas and things of, on, under, relating to, adjacent to, or bordering on a sea, ocean, or other navigable waterway, including all maritime-related activities, infrastructure, people, cargo, and vessels and other conveyances.”

In order to accomplish its research and educational goals, the Center for Secure and Resilient Maritime Commerce (CSR) brings together a unique group of academic institutions and public and private partners that is led by Stevens Institute of Technology, Hoboken, New Jersey. Besides Stevens, the partnership includes the following academic institutions: Rutgers University, University of Miami, University of Puerto Rico at Mayagüez, Massachusetts Institute of Technology, and Monmouth University. The non- university partners in the CSR include the Port Authority of New York and New Jersey, the Mattingley Group, the Pacific Basin Development Council, and Nansen Environmental Remote Sensing Center.

Strategy

The CSR strategy to achieve its mission centers on the creation and sustainment of a truly collaborative research and education enterprise that draws on the strengths of each partner, as well as their leveraged relevant DHS and non-DHS research and education activities. We believe that these unique attributes – collaborative; integrated research & education; and leveraged relationships with Federal, State, local government, and industry stakeholders – positions the CSR for continued long-term success and impact. Going forward, the CSR research and educational activities will continue to be built around two primary areas of interest: 1. Maritime Domain Awareness (MDA) – the development of sensor technologies, analysis tools, and decision aides that can enable an effective understanding of anything associated with the Maritime Domain that could impact the security, safety, economy, or environment of the United States. 2. MTS Resiliency – the development of models, tools, and decision aides that can assist policy makers and decision-makers responsible for making organizational changes and resource allocations to enhance resiliency in our nation’s MTS.

Through the first 4 years of operation, the CSR efforts related to technology development in support of MDA have been guided by a Spiral Development approach to solving the complex issues facing the MTS. This approach, illustrated in Figure 1, has required that the individual research projects – in satellite, radar, and underwater acoustics sensors - achieve the necessary fidelity to provide improved understanding and capabilities, as well as technology products that can be examined via field experiments in the real-world environment. These activities have led to the transitioning of new technologies to beneficial use in the field by the US Coast Guard, US Navy, NOAA, NGA, and the City of New York, among others. They have resulted in the CSR being awarded the DHS S&T Impact Award in two of its first three years of existence.

Figure 1 – Spiral Development Process for CSR R&D

Ensuring Impact The CSR researchers and educators are committed to ensuring that our new knowledge, technology products, tools, and educational programs are responsive to the wide range of stakeholders that we serve. Success in this endeavor requires that we: Ø Partner with the end users to understand their needs; Ø Partner with industry, as well as government and university laboratories, to understand technology gaps and to work toward filling those gaps.

We have already established strong ties with many key end-users. We have visited numerous ports, agencies, and laboratories, including USCG Research and Development Center; US Coast Guard Headquarters in DC; US Coast Guard Sectors New York; Miami; San Juan; Honolulu; and San Francisco; the Port of Los Angeles, Port of Long Beach, Port of Hueneme, Port of Seattle, Port of Tacoma, Port of New York/New Jersey, Port of Houston, Port of Miami, and the Port of San Juan; NYC Office of Emergency

10 Management; NYPD; NJ Office of Homeland Security and Preparedness; the Regional Catastrophic Planning Team in Manhattan; the Naval Research Laboratory, DC; National Maritime Intelligence Center; Naval Undersea Warfare Center, Newport, RI; Naval Surface Warfare Center, Panama City FL; Naval Research Laboratory, Stennis MS; SPAWAR, San Diego, CA; USCG R & D Center, Groton CT; CBP Air and Marine, Miami, FL; JIATFS, Key West, FL; Navy TENCAP; NGA; DIA; Air Force TENCAP; ARSTRAT; STRATCOM; NORTHCOM; Office of Naval Research, Arlington, VA; and US SOUTHCOM, Miami.

In preparation for our Engage to Excel (E2E) efforts in year 5, we have translated these visits and contacts into on-going partnerships – especially in NY/NJ harbor and Puerto Rico.

Transitioning Successful transitioning of Intellectual Property begins with reaching out to the end user, understanding their needs, and building the trust necessary to enable the “productization” of a prototype device, model, tool, or algorithm. It was this sort of outreach in fact that led to the adoption of CSR-developed technologies and algorithms by end users that include the US Coast Guard (Search and Rescue, oil spill response); the US Navy (NUWC, Swimmer Detection System); and the NGA (oil spill response).

Performance Metrics In order to ensure alignment of the CSR research and education activities with the implementation plans under NSPD-41/HSPD-13, our strategy incorporates a set of metrics that serve both to guide the Center’s activities and to facilitate the performance assessment of those activities. Table 2 illustrates how the CSR (MIREES) mission and associated research and education activities align with the implementation plans under NSPD-41/HSPD-13. Table 2 also lists the measureable outcomes – Performance Metrics - associated with the specific CSR activities that are used to assess progress in supporting DHS efforts under NSPD-41/HSPD-13.

NSPD-41/ MIREES Vision CSR Activities Performance Metrics HSPD-13 Statement Goals MDA “...safe and secure MDA sensor 1. # of patent or copyright use of our nation’s technology filings maritime development 2. # of partnerships with domain..” external sensor developers and integrators “..advancement of 3. # of partnerships with the relevant relevant DHS agencies and sciences..” other Federal, State, and Local departments/agencies. 4. # of field evaluations and exercises 5. # of major publications 6. # of technical conference presentations Global “...safe and secure Information 7. # of new algorithms for Maritime use of our nation’s fusion. sensor and information Intelligence maritime Human factors, display/fusion. Integration domain..” including hostile 8. # of new algorithms for “..advancement of intent assessment of hostile intent; the relevant assessment and anomaly detection. sciences..” decision-making Performance Metrics 3, 4, 5, & 6 Maritime “...safe and secure Development of 9. # of new delivery systems to Operational use of our nation’s systems and provide for real-time delivery of Threat maritime algorithms to sensor data, including space- Response domain..” support real- based, land-based, and time Situational underwater sensor information. “..advancement of Awareness/COP Performance Metrics 3, 4, 5, & the relevant 6 sciences..” International “..through Development of 10. # of new research Outreach and advancement of research and collaborations with approved Coordination the relevant educational international partners, as sciences and programs with evidenced by MOUs, MOAs, development of the international and NDAs. new workforce.” partners 11. # of new educational programs with approved international partners. Maritime “...safe and secure Development of 12. # of partnerships with the Infrastructure use of our nation’s models and tools MTS community, including the Recovery maritime to support the port industry, shippers, carriers, domain..” design of a etc.

12 resilient US 13. New data to allow for the “..and a resilient MTS. simulation of the various MTS MTS..” Development of elements of the supply chain. models and tools 14. # of new risk and “..advancement of for use by the vulnerability assessment tools the relevant first responder for the MTS. sciences..” community. 15. # of new systems engineering design tools. 16. # of times direct assistance to the first responder community Performance Metrics 3, 5 and 6 MTS “and a resilient Examine the 17. New data regarding relevant Security MTS,..” national and national and international international policies and regulations that “through policies and impact maritime security. advancement of regulations that Performance Metrics 3, 5 and 6 the relevant impact maritime sciences and security. development of the new workforce” Maritime “and a resilient Develop models 18. New data to populate a US Commerce MTS,..” and tools for the supply chain database. Security analysis of the 19. # of new risk and “through US supply vulnerability models of the advancement of chain, including supply chain, e.g., failure the relevant vulnerabilities to analyses. sciences and disruption. Performance Metrics 3, 5, 6, development of the 12, 13, 14, 17 new workforce” Domestic “..development of Develop/conduct 20. # of new undergraduate Outreach the new educational courses and programs at the workforce” programs and member universities. materials for use 21. # of new graduate courses in K-12, and programs at the member university, universities. public outreach, 22. # of new professional and professional education programs. education. 23. # of new K-12 programs and materials 24. # of new public outreach programs and materials Performance Metric 11 Table 2 – Alignment of CSR Research and Education Activities with NSPD-41/HSPD- 13 Goals; and Project Performance Metrics

13 In addition to these performance metrics, the CSR partners agree that an essential metric of success of the overall Center of Excellence effort is the degree to which the CSR functions as a cohesive, collaborative consortium. To that end, we have also adopted performance metrics that measure the degree of collaboration – by examining the following: • Performance Metric 25: the number of multi-author papers, presentations, and reports • Performance Metric 26: the number of multi-partner papers, presentations, and reports Performance Metric 27: the number of multi-partner educational programs and materials

We will remain flexible as we develop and refine the research plan, with the agility to respond quickly to new and emerging threats and national needs. Throughout, the member universities will involve undergraduate and graduate students, and will develop new courses, new curricula, and professional education and outreach tools to facilitate the training of the next generation of maritime security professionals, including students and professionals from historically underrepresented groups and minority serving institutions.

14 II. DESCRIPTION OF THE CSR

The National Center for Secure and Resilient Maritime Commerce (CSR) brings together a unique set of academic institutions, public organizations, and private sector partners. The Center was created to possess both diverse expertise and significant experience in developing new knowledge, models, tools, policies and procedures, and education/training methodologies related to global maritime security. CSR’s Director is Dr. Julie Pullen, Research Associate Professor of Ocean Engineering at Stevens Institute of Technology. And the PI is Dr. Michael Bruno, Dean of Science and Engineering. The Center is physically located on the Stevens Institute of Technology campus in Hoboken, New Jersey, which is adjacent to the Hudson River and the surrounding New York Harbor. a. CSR Team Members The CSR team consists of academic, public and private sector partners that collectively can address all key areas for DHS. The CSR Team academic members, in alphabetical order, and current principal investigators (PI) are listed below. When there is more than one PI for a school, each current PI’s primary research area or organization is shown in the associated parentheses.

Massachusetts Institute of Technology Mr. James B. Rice, Jr. Monmouth University Dr. Barbara T. Reagor (RRI) Rutgers University Dr. Scott Glenn Stevens Institute of Technology Dr. Jeff Nickerson (Decision-making) Dr. Alexander Sutin (Acoustics) Dr. Brian Sauser (Resiliency) University of Miami Dr. Hans C. Graber University of Puerto Rico Dr. Jorge E. Corredor

The non-academic partners include:

The Mattingley Group Mr. Matt Mattingley Nansen Envir. Remote Sensing Center Dr. Johnny Johannessen Pacific Basin Development Council Ms. Carolyn K. Imamura Port Authority of New York & New Jersey Ms. Bethann Rooney

As stated above, the CSR team was assembled to take advantage of the strengths of each partner to address all aspects of maritime security as defined by our Stakeholders. Several of the partners have worked together for years in numerous U.S. and international projects related to the safe, secure and environmentally responsible transit of maritime cargo and passengers as well as the short and long-term impacts of coastal hazards on socio-economic systems, ecosystems and living marine resources.

The academic partners also have ongoing projects with relevant Federal and State agencies that have resulted in significant related capabilities. Many of these activities

15 have been performed in collaboration, e.g., partnering with national intelligence agencies, and partnering on the NOAA-led Integrated Ocean Observing System (IOOS) initiative. These existing relationships ensure close coordination of the Center’s efforts among a diverse and yet highly complementary group of researchers. In addition to these partners, the CSR Team works with the existing and the other newly formed DHS Centers of Excellence as well as with related institutions, including the Naval Postgraduate School and the US Coast Guard Academy. b. Advisory Board Science and Education Advisory Committee The Science and Education Advisory Committee (SEAC) consists of representatives from the maritime industry, relevant state and local agencies, academia, and national labs. The SEAC advises the CSR on present and future research projects and educational programs from the perspective of the current state-of-the-art in relevant science and technology, and present and future needs of the stakeholder communities. SEAC Members are listed here: v Admiral James Loy (USCG ret), Chair v Ms. Lilliane Borrone, former Director of the Port of New York and New Jersey v Mr. Steven Carmel, VP, Maersk Sealand v Dr. John Montgomery, Director, Naval Research Laboratory v Ms. Sidonie Sansom, Director of Security, Port of San Francisco v Ms. Bethann Rooney, Director of Port Security, Port Authority of NY and NJ v Dr. Martha Grabowski, Professor, RPI

c. Cooperating Programs There are several existing programs that are being leveraged through active cooperation with the CSR. From its Hoboken location, the Center has the opportunity to leverage a significant array of existing sensor and forecasting capabilities in the New York Harbor and its approaches. These facilities and associated equipment are operated by Stevens and Rutgers. Much of the system was funded by the U.S. Office of Naval Research and is currently being entrained by the NOAA Integrated Ocean Observing System (IOOS).

The Stevens New York Harbor Observing and Prediction System (NYHOPS) was established in 2002 to permit an assessment of ocean, weather, environmental, and vessel traffic conditions throughout the New York Harbor region. NYHOPS is the most extensive estuary monitoring and forecasting system in the world, providing real-time observations and 48-hour predictions of ocean and weather conditions throughout the Hudson-Raritan Estuary. The system is used extensively by the maritime community, including the US Coast Guard, NOAA, the Sandy Hook Pilots, the State of New Jersey DOT, and the Port Authority of NY and NJ. The first responder community and emergency management agencies also utilize the real-time observations and the computer model simulations. These agencies include FEMA, DHS, the US Coast Guard, The New Jersey OEM, and New York City OEM. Resource management agencies are also active partners in the program. Some, including the New Jersey DOT and DEP, the New York

16 City DEP, and the National Weather Service, have contributed both funding and guidance in the continuing expansion and enhancement of the program.

On the Atlantic Ocean, Rutgers University’s Coastal Ocean Observation Lab (RU COOL) operates the nation’s most extensive network of High-Frequency Surface Wave Radar (HF Radar) systems. These systems have been extremely successful in providing high-resolution, real-time synoptic information regarding coastal ocean currents along the New Jersey and New York coastal regions. The network is now employed in the USCG search and rescue operations algorithms (SAROPS) in the mid-Atlantic region. Stevens’ New Jersey Coastal Monitoring Network provides real-time observations of weather and ocean conditions on the beach and in the surf zone along the entire New Jersey shoreline.

The University of Miami, Center for Southeastern Tropical Advanced Remote Sensing (CSTARS) is one of the nation’s leading organizations in the development and application of satellite-based tools and products to support various government agencies. CSTARS completed the Foreign SAR Evaluation Project for NGA using RadarSat-2, TerraSAR-X and Cosmo-SkyMed. They have participated extensively in the Foreign SAR Evaluation Project for the National Geospatial-Intelligence Agency (NGA), providing analysis and inter-comparison of targets and AOIs collected with all three sensors. CSTARS also participated in the Tandem Project in support of the USAF Space Missile System Center, acting as an advisor to the USAF in evaluating the usefulness of a tandem mission RadarSat-2 project. As a direct partner with the Defense Intelligence Agency, CSTARS hosted the installation and implementation of the Italian four satellite X-band SAR system COSMO-SkyMed. CSTARS is currently the only foreign ground station with full tasking and reception capabilities for global and local near-real time acquisitions.

To complement the programs cited above and under sponsorship from the U.S. Office of Naval Research, Stevens established the Maritime Security Laboratory (MSL) in 2004. The MSL is viewed as a primary existing facility that can be leveraged to support the CSR goal of establishing a real-world “laboratory” for test and evaluation and experiments and exercises. The MSL provides harbor-wide, coordinated, in-water environment sensors, weather stations, and video cameras (including IR) with pattern recognition software to enable automated surface ship detection and tracking. The MSL also possesses a fully-equipped data fusion and visualization center. Over the past 7 years, MSL researchers have performed experiments related to: • SCUBA diver detection using passive acoustic arrays; for both open-circuit and closed circuit SCUBA; • The acoustic transmission parameters of the estuarine environment; • The surface traffic acoustic signal characteristics and variability; • Computer forecast model – assisted UUV operations for persistent underwater surveillance; • Behavioral analysis of hostile intent; analysis of evasive behavior; and • Optimization of sensor placement

17 d. Collaboration with co-lead CIMES The leadership and researchers at the co-lead institutions - the National Center for Island, Maritime, and Extreme Environment Security (CIMES) and the CSR - believe that collaboration between the two centers is essential to meet DHS’s objectives. To that end, the two Centers set up an initial meeting at the University of Hawaii in November 2008 to share information about the research and education activities at the partner universities from both centers. This meeting was very productive in that there was an open discussion among the participants regarding various activities, and the key researchers for each activity were identified for follow up discussions. This led to another meeting in January 2009 that was hosted by the University of Miami and was used to discuss research collaboration among participants, and several meetings with the Director of CIMES (Dr. Roy Wilkens until the end of 2010 and Dr. Margo Edwards starting January 2011), in August of 2009, and January of 2010 (two separate meetings) to discuss ongoing joint activities and ensure continued partnering. The first full joint meeting of the two centers occurred in January 2011 on the campus of the University of Hawaii. This was followed by a second full joint meeting of the two centers in February 2012 on the Stevens campus. (The presentations from these two events are available on the CIMES and CSR websites.) The collaboration is strong and ongoing between the two Centers. The following areas are highlights of collaboration during year 4 of operation: • Passive acoustics: Researchers from Stevens and the University of Hawaii shared data and information from the work they have been performing in passive acoustics, and discussed ways for improving passive acoustic methods of port protection, including detection, classification and localization of surface ships, divers and UUVs. Several meetings have been held between the two universities’ acoustic research teams, with the notable result that the Stevens team provided the Hawaii team with the detailed technical specifications and design guidance for a passive acoustic system, and the Hawaii team has provided algorithms and signal processing guidance to the Stevens team in order to improve the performance of the existing Stevens systems. We are confident that this joint effort will prove remarkably successful in the coming years in terms of providing the community with a passive acoustic surveillance capability that does not presently exist. The steps to achieve this were outlined in a workshop organized by CIMES and attended by CSR researchers entitled, “Persistent Approaches for Detecting Small Vessel Traffic” February 16 - 17, 2012 at the U.S. Coast Guard base - Alameda, California. • HF Radar Surveillance: The HF Radar teams at Rutgers University and the University of Alaska (CIMES partner) conducted a joint Alaska deployment in summer 2012. • Power Systems for Extreme Environments: Researchers from the University of Alaska and Stevens have discussed various battery technologies and the requirements for robust and efficient means to provide power and back-up power in extremely cold and isolated environments, such as Alaska. • Autonomous Vehicles: Stevens and the University of Hawaii researchers Yi Gou and Brian Bingham jointly applied for and were awarded an NSF grant for “Distributed Heterogeneous Ocean Robots for Detecting and Monitoring Oil Plumes.”

18 e. Communications and Outreach Collectively, CSR and CIMES represent a unique consortium of academic institutions, working in close collaboration and partnership with several industry and government partners. It is MIREES’s shared vision to function as a key national resource to DHS in all areas of maritime security and coastal, island, and inland waterway maritime domain awareness, through innovative research and relevant educational programs. To that end, MIREES strives to reach its vision through the combined strength and capabilities of its partnerships and through efficient and effective communications and outreach to its stakeholders, end-users and the general public.

Since MIREES’s inception in 2008, CSR and CIMES have largely developed their own communications and outreach initiatives and have maintained separate websites. However, in year 4 the centers adopted a branded e-mail system, EMMA, and joint database for shared distribution of invitations, newsletters, etc. This approach has been highly successful and we continue to build on it. In addition CSR and CIMES have jointly participated in OUP-organized Stakeholder events for entities including Coast Guard and DHS I&A. f. Collaboration with other COEs The CSR is partnering with the COE co-led by Rutgers University and Purdue University, the Command, Control, and Interoperability Center for Advanced Data Analysis (CVADA). This collaboration is aimed at enabling new techniques and algorithms for the analysis of multi-sensor data being gathered by the CSR researchers, including space-based, HF Radar, and passive acoustic data. The collaboration may also include researchers at CSR and CVADA working together on decision-making, particularly in the study of decision-making in complex (data-rich) environments. CSR researchers are also partnering with researchers from CREATE in the area of maritime risk assessment and port system resilience, whose workshops we participate in. And we have had preliminary conversations with START on contributing to their educational offerings for first responders.

III. RESEARCH PROJECT REPORTS

a. Description of Research Areas As mentioned earlier, CSR, along with the University of Hawaii’s National Center for Islands, Maritime, and Extreme Environments Security (CIMES), support DHS efforts under NSPD-41 / HSPD-13 to provide for the safe and secure use of our nation’s maritime domain (including island and extreme environments and inland waterways), and a resilient MTS, through advancement of the relevant sciences and development of the new workforce. The CSR research efforts have been divided into two basic areas: Maritime Domain Awareness (MDA), and Port Resiliency. These research areas, and their associated tasks (projects) can be summarized as follows:

1. Maritime Domain Awareness (MDA):

Task 1.1 Space-Based Wide Area Surveillance

Task 1.2 Investigation of HF Radar for Multiple Applications: Over-The- Horizon Vessel Detection and Tracking, Search and Rescue, and Environmental Monitoring

Task 1.3 Near-shore and Harbor Maritime Domain Awareness Via Layered Technologies

Task 1.4 Decision-Support Systems

2. MTS Resiliency

In the following, we provide a summary of the research efforts and results associated with each of these projects.

20 1. Maritime Domain Awareness

Task 1.1 Space-Based Wide Area Surveillance, University of Miami (H. Graber, PI) TRL 8

Project Objectives and Significance to Stakeholders

CSTARS continues to explore the use of space assets such as high-resolution electro- optical (EO) and synthetic aperture radar (SAR) for detecting and classifying small boats to large vessels. Also SAR data is extensively applied to monitoring of environmental pollutants such as oil spills or algae blooms that potentially can have negative impacts on port and maritime commerce as well as pose health threats to coastal communities. On- going studies and exercises with DOD agencies show that wakes from small, fast moving boats can be detected as well as large vessels can be tracked across ocean basins. In collaboration with one of the primary companies operating a network of spaced-based Automatic Identification System (AIS) transponders we have made tests of monitoring large ocean basins with space-based AIS. Also CSTARS demonstrated its capability to rapidly respond and deliver large volumes of images for the DeepWater Horizon oil spill in the Gulf of Mexico. Specifically, CSTARS will continue to participate in port and maritime security exercises and leverage other resources in achieving secure operations within the global and national maritime domain. In year 4, although not initially planned, we rapidly responded and supported the U.S. Coast Guard in some initial monitoring of the exploratory drilling for oil off the Cuban coast. This imagery was able to demonstrate timely and actionable data collection for a stakeholder agency, and the imagery was subsequently used in testing algorithms under development for change detection.

Key Project Objectives include: 1. Continued tests of spiral development with other CSR teams in New York Harbor and/or other collection opportunities. 2. Explore amplitude change detection on successive pairs of imagery to determine new ship arrivals and old ship departures in port facilities. 3. Further development of radon transform for use of extracting ship wakes in noisy environments. 4. Explore the informational content of SpotLight, StripMap and ScanSAR modes on the accuracy of detecting vessels and the fidelity of discriminating vessel characteristics (i.e., container, cargo, tanker, fishing vessels, etc). 5. Continued detailed examination of the value of polarimetric (dual-pol and/or quad- pol) data from the new SAR sensors to improve vessel and wake detection.

Milestones Met

1. Collection Opportunities

Several collections of opportunity led to high-quality imagery for testing algorithms. This milestone was achieved.

21 This section describes a collection opportunity of interest to the USCG for monitoring and change detection to provide information on structures and contaminants in the event of an oil spill. It also provided a test of change detection algorithms. Recently Cuba has embarked to develop its offshore oil resources and signed lease agreements with several foreign companies to begin exploratory drilling along Cuba’s north side. The U.S. Geological Survey estimates that offshore oil reserves could hold as much as 4.6 billion barrels and could technically be extracted. In late January 2012 a mobile offshore drilling unit (MODU), Scarabeo-9, arrived in the North Cuba Basin and will start drilling in the Jagüey Prospect in 5,300 feet of water to penetrate the earth as deep as 20,000 feet below the sea bottom. Initially consortium led by Repsol, a Spanish-owned company, with Statoil from Norway and Oil and Natural Gas Corporation from India are expected to begin offshore exploratory drilling in early 2012. Additional foreign companies that have lease agreements with Cuba are Venezuela's Petroleos de Venezuela S.A. (PDVSA), Malaysia's Petronas, Vietnam's Petrovietnam and Angola's Sonangol. Currently China's National Petroleum Company (CNPC) is negotiating with Cuba for an offshore block. Figure 1 shows Cuba’s three areas comprising the North Cuba Basin assessed by the USGS with potential oil holdings. The inset on the upper left is a photo of the Scarabeo 9 mobile drilling unit. The blue dot shows the approximate location offshore of Havana where drilling took place.

Figure 1: North Cuba Basin. The Scarabeo 9 will start drilling at approximately 23.24o N, 82.39 W. Source: U.S. Geological Survey, “Assessment of Undiscovered Oil and Gas Resources of the North Cuba Basin, 2004,” (February 2005). http://walrus.wr.usgs.gov/infobank/programs/html/factsheets/pdfs/2005_3009.pdf. Note: “AU” = Assessment Unit.

With the Deepwater Horizon (DWH) oil spill in the Gulf of Mexico still fresh in the public’s mind, some Members of Congress and especially the people of South Florida

22 have voiced concerns about Cuba’s and the foreign drilling companies’ preparedness to respond to an oil spill. In the event of an oil spill in the waters north of Cuba, the Florida Current and its eddies could transport the oil to U.S. waters and ecologically sensitive coastal areas such the Florida Keys and Florida Bay as well as to white sandy beaches along the coast of Florida and the Mid-Atlantic Bight states. The risk of severe environmental impact in southern Florida is very high if significant amounts of oil would reach U.S. coastal waters.

USCG asked CSTARS to make some initial collects on the new location of the Scarabeo 9 about 225 miles southwest of Key West (Figure 2). On May 27, 2012

225 miles

o 22 41’ 20” N o 84 43’ 08” W

Figure 2: North Belt Trust in Florida Straits is the new site for Scarabeo 9 to commence drilling.

CSTARS acquired a high-resolution (70 cm) panchromatic optical image of the Scarabeo 9 at its new location (Figure 3). The image confirmed the location of the drilling unit and the presence of two support vessels, “Bourbon Liberty 115” and “E.R. Stavanger”. Also visible are the helipad, the deck crane and ram rig in the center of the mobile drilling unit. Two days later CSTARS acquired a spotlight (1 m resolution) TerraSAR-X synthetic aperture radar (SAR) image on 29 May 2012 (Figure 4). While this image showed the basic features of the oil rig and the presence of also three vessels, but this time the “Bourbon Liberty 108” was either leaving or examining the lubricant surface streak. The support vessel “E.R. Stavanger” had departed and was heading to Mariel, Cuba. The information of the vessels in the vicinity of the Scarabeo 9 was provided from a constellation of satellite AIS data streaming live at CSTARS. The SAR image also revealed surface slicks which are similar to an oil slick. However, additional information revealed that the surface streaks were lubricant fluid from the drilling operation and not oil.

23 Figure 5 shows the nearly full TerraSAR-X image with several streaks and blobs of drilling lubricant are visible on the surface. The primary streak from the drilling unit is more than 6.6 km long with two blobs occurring at about the midpoint of ~800 m to 900 m long and ~550 m to 600 m wide. No additional images were taken after these dates.

Bourbon Liberty 115

Helipad

Deck Crane

Ram Rig

E.R. Stavanger

Figure 3: EROS-B panchromatic image taken on 27 May 2012 @ 19:12:50 GMT of the Scarabeo 9 oil drilling rig. This high resolution image (70 cm) reveals several details of the oil drilling rig as well as the presence of two support vessels.

24 Bourbon Liberty 115 Drilling lubricant

E.R. Stavanger

Bourbon Liberty 108 Deck Crane

Drilling lubricant Scarabeo-9 oil drilling rig

Figure 4: TerraSAR-X spotlight radar image taken on 29 May 2012 @ 11:44:48 GMT of the Scarabeo 9 oil drilling rig. This high resolution image (1 m) shows in addition to the presence of three vessels, surface features, dark streaks, emanating from the drilling unit.

Multiple lubricant streaks

Lubricant leakage Length: ~6.6 km Width: ~70 m

Lubricant blob 800 m x 560 m

Lubricant blob 890 m x 590 m

Lubricant streak

25 Figure 5: TerraSAR-X spotlight radar image taken on 29 May 2012 @ 11:44:48 GMT of the Scarabeo 9 oil drilling rig. This high resolution image (1 m) shows in addition to the presence of three vessels, surface features, dark streaks, emanating from the drilling unit.

Another collection of opportunity occurred as part of the DHS Summer Institute at Stevens Institute of Technology, Hoboken, NJ on 13 – 16 June, 2011. Dr. Graber presented lectures on satellite remote sensing and worked with the Satellite Team to develop an image collection plan for several days to monitor the vessel traffic in the morning and evening along the Hudson River.

2. Amplitude change detection of port facilities and choke points

This robust capability was developed and demonstrated for multiple datasets and achieved the successful application of successive pairs of imagery to accurately determine ship arrivals and departures. . One approach to monitor the comings and goings of vessels in port facilities or the transit of vessels through choke points is with amplitude change detection (ACD) of SAR images. ACDs are a valuable product to monitor the departures / arrivals and on-load / off-load operations at port facilities. Using successive geo-registered pairs of imagery of a port can be applied to determine the arrival of new ships and the departure of old ships. The more frequent this can be accomplished the better is the time history of how quickly vessels are loaded and unloaded. How much time is needed can also help to determine to type of cargo if not obvious such as an oil tanker, container ship or LNG tanker.

Figure 6 shows an example using Cosmo-SkyMed spotlight imagery amplitude change detection of Puerto Cabello, Venezuela. The ACD is derived from two images collected three days apart. Departed vessels are shown in red, while vessel newly arrived are indicated in blue. Note that cargo holding areas also show changes in red and blue indicating that loading and off-loading operations commenced during the time period these two images were taken. Clearly, ACDs easily detect the departure and arrivals of vessels. Vessels appearing in grey were present in both images.

Figure 6: Two Cosmo-SkyMed images collected three days apart were used to generate this Amplitude Change Detection product of the port facilities at Puerto Cabello, Venezuela. Departed vessels are show in red and arrived vessels are in blue. Vessels in grey shades with spots of red and/or blue mean that they have been in port for at least three days, but undergoing on-loading and/or off-loading operations.

3,4,5.Developing new algorithms for ship and wake detection (multi-method work in progress)

The algorithm development has matured to the point of operational utility in a variety of contexts (wake detection, vessel characteristics). This direction and progress on research has had significant success in extraction of ship wakes in a noisy (background) environment. Also, successful testing and evaluation of two imaging modes, including the Large Maritime Beam Mode and the Very High-Resolution Spotlight Mode was documented. And successful determination of the accuracy of vessel position provided by image-based location was shown. See details below.

We continue to work with the Radon Transform (RT) mapping of the spatial domain of an image into the two dimensional Euclidean space (Radon, 1917). The RT is defined as

27 where g(x,y) represents the intensity level of the image at position (x,y), δδ denotes the Dirac delta function, ρ the length of the normal from the origin to a straight line, and θ represents the angle between the normal and the x-axis. The Dirac delta function forces the integration of g(x,y) along the line ρ = x cosθ + y sinθ. Consequently, the RT yields the integrals across the image at varying orientations θ and offsets ρ relative to the image center (Murphy, 1986: Murphy, L.M., 1986. Linear feature detection and enhancement in noisy images via the Radon Transform. Pattern Recognition Letters, 4 (4), 279–284. Radon, J., 1917. Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Mathematisch Physikalische Klasse, 69, 262–277). The main advantage of this operation is that it maps a straight line in the image space into a single point in the transform space. Detection of dark (bright) linear features in an image thus becomes detection of dark (bright) points in the Radon domain.

Figure 7 shows an ERS-2 image with a vessel (bright spot) and its wake (dark linear feature (left). Since bright pixels can detract and mask dark wakes in SAR images, we either shift the image until the bright target is removed or only select the image domain that contains the wake (right). Now only the dark wake feature is in the image.

Figure 7: Left: ERS-2 image of a ship and its dark wake. Right: Image is shifted to remove ship from image. Now only the wake and background remain in image.

The image portion that only contains the wake and background is then processed with the Radon Transform. Figure 8 presents the Radon Transform of the right side image in Figure 7. Since the wake is a dark linear feature we look for troughs in the Radon space as indicated by the two red circles. These two positions provide the orientation or angle of the wake in the image.

28 In order to explore SAR images more quantitatively like extracting the length of a vessel, or discriminate targets near other strong reflectors such as small boats in ports or near docks and berths a better filtering approach of pixels is needed. Radar images have varying speckle (noise) characteristics that depend on the background and ambiguities from stronger reflectors but far away. Bilateral filtering was originally developed to filter optical images (Gaussian additive noise). We adapted the filtering approach to SAR/speckle statistics. It uses a standard spatial filter along a radiometric component. The radiometric component weights neighborhood pixels based upon radiometric "closeness". The result of this approach is that it preserves edges and details better than standard types of filters.

Values

512 0 33

Rho (pixels)

Since wake is dark, look for troughs in RT. -512

0 180 360 Theta (degrees) Figure 8: Radon Transform of the ERS-2 image without the ship and only of the dark wake. The red circles marking troughs in the transform and since wake is dark also indicate the angle of the wake in the image.

The bilateral filtering approach is developed on the use of a standard spatial filtering kernel given by

! ! !!!!!! !! !! where !d is the spatial standard deviation! !! ! ! with! ! d(x,y) the Euclidian distance defined as

for the simplified 1-dimensional case!. ! ! ! ! ! ! ! !

The radiometric filtering kernel is defined by

! !(!,!) ! ! � �, � = � ! !! where σr is the noise standard deviation with δ(x,y) = radiometric distance defined as

δ �, � = δ(�) − δ(�)

For SAR images the radiometric portion of the bilateral filtering needs to be changed to consider the following:

• SAR speckle is not additive and does not follow Gaussian statistics • For single look amplitude data the Gaussian model can be replaced with a Rayleigh model • For single look intensity data the Gaussian model can be replaced with a negative exponential model • For multi-look intensity data the Gaussian model can be replaced with a Gamma model

For the data type we are using the single look amplitude radiometric filtering kernel would be expressed as

� ! � �, � = 2 �!! /!! � where σr is best estimated by the square distance of the neighboring pixels.

Figure 9 shows a typical SAR image of a coastal scene with some bright targets and diffuse boundaries and edges.

30 Figure 9: A typical SAR image of a coastal scene with bright targets and diffuse boundaries and edges.

Figure 10 shows the difference of the standard filter and the bilateral filter applied to the image above. Because of the radiometric component neighborhood pixels are weights based upon radiometric "closeness". This weighting preserves edges and details much better than standard types of filters.

Standard Bilateral Filter Filter

Figure 10: Left: Standard filtering technique applied to image in Figure 9. Features and background get all smoothed or smeared with little visible detail. Right:

31 Bilateral filtering of the same images preserves edges and boundaries as well as small features better.

Figure 11 shows a Cosmo-SkyMed stripmap image of vessels in the Chesapeake Bay. Besides a clear identification of two vessels, the image also delineates theirs wakes and the Doppler shift from the wake. The course change of the second vessel is still visible in the image taken minutes to hours afterwards. Using the Doppler shift of these two vessels we calculate a speed of 9.0 kts for Ship 1 and 15.8 kts for Ship 2. From AIS at the time of the image we extract a speed of 11.2 kts and 17.0 kts, respectively. Differences in bearings for both vessels determined from the image or AIS is only 1 degree. The estimate of the ship length is within 10% of the true length.

Figures 12 and 13 show two examples of different types of ships detected with high resolution SAR imagery. Both images were taken with Cosmo-SkyMed spotlight mode and reveal enough details to classify the vessel. In Figure 12 the vessel is an oil tanker with a support ship adjacent to the tanker for off-loading (pumping) of oil. The central pipeline structure and the superstructure are clearly visible. In Figure 13 two container vessels are detected showing the outline of standard 20 ft containers and the location of the superstructure.

North

Shift ~470 pixels Bearing ~78.0 deg

Ship 2 Length ~229 m

Shift ~248 pixels

Bearing ~ 60.0 deg Ship 1 Transverse wake Length ~276m Course change

Kelvin wake

Figure 11: Cosmo-SkyMed stripmap image collected over the Chesapeake Bay showing two vessels. The image clearly depicts the vessels’ wakes and even course change. Both vessels are Doppler-shifted from their wake. This displacement in pixels is used to determine the speed of the vessel. Note that for Ship 2 both Kelvin arms of the wake and even the transverse wake pattern is visible in the radar image.

32 Cosmo-SkyMed

Central pipeline structure Second ship for harbor support

Oil tanker

Command superstructure at the end

Access to pump oil into single oil tanks

Figure 12: Cosmo-SkyMed spotlight image showing an oil tanker and its support vessel. The SAR image reveals the ship’s superstructure, central pipeline structure and pumping access to offload oil. Cosmo-SkyMed

Command superstructure almost in the center

Standard 20-foot container lines

Cargo ships

Figure 13: Cosmo-SkyMed spotlight image showing two container vessels and well defined standard 20 ft container lines.

Outcomes

33 1. Resources Leveraged

1.) NGA’s CSTARS Commercial SAR Pilot Project 2.) NGA’s Crisis Support Project 3.) NGA’s JCTD CROSS 4.) Cooperative maritime support from JIATFS, ICE and USCG 5.) ONR projects on relevant maritime problems

2. Publications

Several published papers resulted from the work in year 4. The first two papers focus on the algorithm development for wake detection. The third publication describes CSR’s layered MDA approach for focused experiments.

Soloviev, A., M. Gilman, K. Moore, K. Young, and H.C. Graber, 2008: Hydrodynamics and remote sensing of far wakes of ships. SEASAR 2008: The 2nd Int. Workshop on Advances in SAR Oceanography, Frascati, Italy, January 21–25, 2008.

Gilman, M., A. Soloviev, H.C. Graber, 2011: Study of the far wake of a large ship. J. Atmos. Oceanic Techno., 28(5), 720-733.

Bruno, M., A. Sutin, K.W. Chung, A. Sedunov, N. Sedunov, H. Salloum, H.C. Graber, P. Mallas, 2011: Satellite imaging and passive acoustics in layered approach for small boat detection and classification, Mar. Techn. Soc. J., 45(3), 1-11.

34 Task 1.2 Investigation of HF Radar for Multiple Applications: Over-The-Horizon Vessel Detection and Tracking, Search and Rescue, and Environmental Monitoring (Note: there are two PIs on Task 1.2, S. Glenn, Rutgers; and J Corredor, UPRM) TRL 8

Task 1.2, Project 1, Rutgers University (Dr. Scott Glenn, PI)

Project Objectives and Significance to Stakeholders

Year 4 Objectives: • Examine the HF Radar data processing sensitivities in the Approaches to New York Harbor Mid-Latitude Testbed to determine the desired range of settings for real-time operations. • Implement a real-time mono-static HF Radar vessel detection capability in the Approaches to New York Harbor Mid-Latitude Testbed where the vessel detections are calculated in real time at a remote shore site and transferred to a data aggregation center. • Work with the University of Puerto Rico to develop the multi-static HF Radar capability in the Puerto Rico Tropical Island Testbed. • Collaborate with CIMES to design an Arctic High-Latitude Testbed for HF Radar vessel detection. • Collaborate with DoD efforts to develop the multi-static HF Radar capability in the Approaches to NY Harbor Mid-Latitude Testbed.

As identified in the May 2009 High Priority Technology Needs, these are the areas that we are looking to improve upon with this research:

• Border Security - Detection, tracking, and classifying of all threats along the terrestrial and maritime border • Border Security - Improved analysis and decision-making tools that will ensure the development and implementation of border security initiatives • Maritime Security - Wide-area surveillance from the coast to beyond the horizon, including port and inland waterways, for detection, ID, & tracking— In particular, the detection of vessels between the port region and beyond the horizon, especially small vessels with the capability to geo-reference the images (Borders and Maritime Security Division) • Maritime Security - Improved radar performance for detection and tracking of large and small vessels in the port and coastal regions— in particular, through the use of more advanced signal processing

Milestones Met

Rutgers defined five tasks during the progress period. The results of the five tasks are outlined here:

1. Operate 13 MHz shore based network with AIS receivers.

Over 80% data return rate was a metric of success. In fact, this metric was exceeded. The radar at Sea Bright, NJ operated with 95% data return rate (Figure 1). The vessel detection data is archived at Rutgers starting on October 1, 2012. We added a second real time vessel detection license at Belmar in February 2012.

Figure 1: Screen shot of the diagnostic page from the Sea Bright 13 MHz radar. The radar provided 95% data return from July 1, 2011 to June 30, 2012.

We created a database to hold the AIS and vessel detection data. We improved the coverage of the AIS network in this past year. We now have seven AIS receivers located in the New York Bight. One station is located in New York in Hempstead and the remaining sites are in New Jersey (Sea Bright, Belmar, Loveladies, Strathmere, Tuckerton and Wildwood). Please see Appendix A for monthly coverage plots for the AIS network.

Figure 2: Map of the Rutgers AIS network. The location of the receivers are indicated by the triangles. Ocean bathymetry and shipping lanes are also shown on the map.

Created web interface to view and filter the vessel detection data. The web interface consists of four sections A. Data Filter – Here the user can decide what data is shown. The user has the option of plotting the data from the monostatic or bistatic returns. The user can decide what FFT length or background type to plot. The user can filter the data by signal to noise (SNR) threshold and time. You also have the option of coloring the detections by SNR, radar cross section (RCS), range, velocity and bearing. B. Parameter Plots – here the detections are plotted versus time. The subplots from top to bottom are range (km), radial velocity (m/s) and bearing (degrees). C. Map Plot – here the detections are placed on a map of the approaches to New York Harbor with shipping lanes displayed on the map. D. Data Display – here the information from the individual detection is outputted to the screen. The useful part of this interface is the ability to correlate detections across visualization methods. In other words, the user can place the mouse over an individual data point in any of the three plots in section B or the map in section C and the corresponding data point is highlighted in the other three plots. This will assist in the goal of increasing the probability of detection and lowering false alarm rates.

The interface is password protected and available for use at: http://marine.rutgers.edu/~michaesm/sandbox/vessel_detection/

The interface was piloted as part of the 2012 Summer Research Institute.

37 Figure 3: Screen shot of the web interface for the display of the real time vessel detection output.

2. Conduct bistatic vessel detection test

There are significant technical challenges to a bistatic shore-sea test. The utility of this configuration was successfully demonstrated. In August 2011 Rutgers began operating a multistatic 13 MHz network off the coast of NJ in support of the Ocean Power Technology Littoral Expeditionary Autonomous PowerBuoy (LEAP) program and the Department of Homeland Security Center of Excellence. Figure 4 gives the location of the three shore stations (SEAB, BELM and SPRK) and bistatic buoy (OPTB) that were used as part of the experiment. Table 1 provides the list of transmit stations, receive stations and the site codes for the resulting data streams. The naming convention for the bistatic site codes are the first two letters represent the receive station and the last two letters represent the transmit station. These four stations will generate nine data streams. Table 2 provides the alignment or timing values used to synchronize the signal between all the stations.

Figure 4: Map showing the location of the three SeaSonde stations and offshore bistatic transmitter that will be operated for the LEAP program.

Table 1: Bistatic combinations and four letter site codes for these data streams. SET1 was in operation from July 21, 2011 0900 UTC till August 12, 2011 0600 UTC.

Transmit Stations BELM SEAB SPRK OPTB BELM BELM BESE BESP BEOP SEAB SEAB SET1/SESP SEOP SPRK SPRK SPOP Receive Stations

Table 2: GPS alignment numbers needed ensure that the stations are bistatically linked and not interfering with each other. Site Alignment (ms) BELM 13694 SEAB 14438 SPRK 15210 OPTB The US Coast Guard deployed the bistatic transmitter on Thursday August 11, 2011. The transmitter operated from August 11 till the buoy was removed from the water on October 31, 2011 (81 days).

39 We would like to discuss one test case that highlights the benefit of an at sea bistatic transmitter. We processed four hours of range data (1000 to 1400 UTC) from the SEAB station on September 30, 2011. The monostatic detections from the site are given in Figure 5. There are several targets present in the figure. We recorded AIS signals at the SEAB station as well and have the track of the vessel Amalthea to compare the radar detections against. The Amalthea (Figure 6) is a tanker with a length of 247 m and breadth of 40 m. The track of the Amalthea is shown as the aqua line in the range, velocity and bearing plot of Figure 5 and the red line of the map in Figure 7. The bistatic detections of the Amalthea transmitted from the LEAP buoy are shown in Figure 8.

We used the AIS signal to validate the detections by the radar. The radar made 77 detections of the vessel in monostatic mode (Figure 9) for a detection rate of 45.8% and 111 in bistatic mode (Figure 10) for a 68.5% detection rate. There were 62 detections that were made at the same time. Utilizing both signals increases the total number of detections to 126 and a 76% detection rate.

From Figure 5 the detections of the Amalthea in range reach a maximum of 23 km. The range measurement of the bistatic detections in Figure 8 is not radial range from the receiver at Sea Bright but elliptical range between the receiver at Sea Bright and the LEAB buoy. This elliptical range is proportional to the time delay of the signal being generated at the buoy, reflecting off the target and being recorded at the receive station. The bistatic range of the Amalthea remains relatively constant from 12:00 to 14:00 as shown in the top panel of Figure 11. This is confirmed by the fact that Amalthea would have remained in the second time delay cell as it transited southeast through the Hudson Canyon shipping lane (Figure 11).

The benefit of the LEAP buoy is displayed by the fact that detections were made on the Amalthea from 13:15 to 14:00 as shown in Figure 8. Although the AIS signal from the Amalthea drops out at 13:00 and we are unable to verify the accuracy of the radar detections, if you extrapolate by eye the aqua line in Figure 8 the detections from the radar would overlay nicely with the extrapolated track of the Amalthea. If you place the detections from Figure 8 onto a map as in Figure 12 you can see that the detections extend out to 40 km from the Sea Bright radar station, 20 km further than the monostatic detections.

40 Figure 5: Monostatic vessel detections from the SEAB radar station on September 30, 2011. These detections are for the median background, 256-point FFT and 11 dB threshold. The track of the Amalthea is shown as the aqua lines.

Figure 6: Picture of the Amalthea a tanker with a length of 247 m and breadth of 40 m

41 Figure 7: Track of the Amalthea (red line) from ~ 11:00 to 13:00 on September 30, 2011. The Nantucket, Hudson Canyon and Barnegat (clockwise from right) shipping lanes are shown in the bottom right. The Amalthea left New York Harbor and travelled southeast along the Hudson Canyon shipping lane. Range (km) from the Sea Bright radar station is shown as the blue circles.

42 Figure 8: Bistatic vessel detections transmitted from the LEAP buoy received at the SEAB radar station on September 30, 2011. These detections are for the median background, 256-point FFT and 11 dB threshold. The track of the Amalthea is shown as the aqua lines.

Figure 9: Association of the monostatic detections with the AIS data of the Amalthea. The detection rate over this time period was 45.8 %.

Figure 10: Association of the bistatic detections with the AIS data of the Amalthea. The detection rate over this time period was 68.5 %.

43 Figure 11: Bistatic time delay cells between the Sea Bright Tx/Rx station and the LEAP Tx buoy. The Nantucket, Hudson Canyon and Barnegat (clockwise from right) shipping lanes are shown in the bottom right.

Figure 12: Bistatic vessel detections (dots color coded by the SNR on channel 3) from the LEAP buoy placed on a map. The locations of the radar stations are

44 shown as the large red circles. The range (km) from the Sea Bright radar station is indicated by the large black circles.

Multistatic detections of vessels were made in the Rutgers 13 MHz testbed. We were able to make bistatic detections from signals originating from the LEAP offshore buoy as well as other transmit stations on shore. The use of multistatic signals increases detection rates by filling in gaps when targets pass through the Bragg region or zero Doppler as well as extending the range of the detections from a receive station.

3. Design multistatic New York Harbor network

The network design was achieved and implemented (see 4). We present six scenarios for the combinations of transmitters and receivers for ship detection, which are summarized in Table 3. We constructed software that would visualize the expected coverage of the monostatic and bistatic combinations of radars. We are in the process of refining this software to calculate the coverage impact as we move systems around the network in order to optimize it. Rx1 and Tx1 represent the SEAB site, Rx2 is the BELM site and Tx3 is the LEAP transmitter.

The expected coverage of the backscatter system is shown in Figure 13. This figure assumes a target/power/noise scenario such that a vessel can be detected to 55 km at 13 MHz with 10 dB SNR. This is realistic with the normal SeaSonde settings with a target radar cross-section of 30 dBsm and a sea state with a significant wave height of 0.9 m. The outer detection limit for this and the following figures was set to 10 dB. The signal strength map for two shore stations separated by 40 km and linked bistatically is given in Figure 14. The expected coverage utilizing an at sea bistatic transmitter is shown in Figure 15. Every parameter in the radar equation remains constant for the bistatic case with the exception of the path losses, which are recalculated for the bistatic geometry. Combining the coverage from the backscatter and bistatic system yields the coverage map shown in Figure 16. The signal strength for two SeaSonde stations each receiving signal from an at sea bistatic transmitter is shown in Figure 17. Lastly, the signal strength for two SeaSonde stations each receiving signal from an at sea bistatic transmitter and the shore stations are each bistatically linked is shown in Figure 18. Assuming a straight coastline along the y-axis and ignoring the coverage to the left of the coastline, the spatial coverage out to the 20 dB contour for these six scenarios is given in Table 3. The coverage is nearly doubled with the addition of an at sea bistatic transmitter. The addition of a component increases the coverage in each case with the exception of Scenario 6, where the addition of the bistatic link between the shore stations does not increase coverage but does increase data quality near the stations. Table 3: Six transmitter (Tx) and receiver (Rx) combinations for vessel detection with the corresponding expected coverage area for the 20 dB contour.

45 2 R x1Tx1 R x2Tx2 R x1Tx3 R x2Tx3 R x1Tx2 Area (km ) Scenario 1 Figure 13 ● 1800 Scenario 2 Figure 14 ● ● ● 3600 Scenario 3 Figure 15 ● 3400 Scenario 4 Figure 16 ● ● 4600 Scenario 5 Figure 17 ● ● ● ● 6600 Scenario 6 Figure 18 ● ● ● ● ● 6600

Figure 13: Signal strength map for a backscatter 13 MHz SeaSonde. The green lines are contours of signal to noise (dB).

Figure 14: Signal strength map for 2 backscatter and 1 bistatic 13 MHz SeaSondes. The green lines are contours of signal to noise (dB).

Figure 15: Signal strength map for an at sea bistatic transmitter located 70 km from the shore station. The green lines are contours of signal to noise (dB).

Figure 16: Signal strength map for an at sea bistatic transmitter paired with a 13 MHz SeaSonde Tx and Rx. The green lines are contours of signal to noise (dB).

Figure 17: Signal strength map for an at sea bistatic transmitter paired with two 13 MHz SeaSondes. The green lines are contours of signal to noise (dB).

Figure 18: Signal strength map for an at sea bistatic transmitter paired with two 13 MHz SeaSondes. The two SeaSondes are also bistatically linked. The green lines are contours of signal to noise (dB).

4. Conduct multistatic real time vessel detection test

The real time test was successfully conducted June 11, 2012. This was used as an exercise with the HF radar team of the 2012 Summer Research Institute. A total of 252 vessels were recorded on the Rutgers AIS network for this day. We then filtered the data by range, bearing and radial velocity. Only vessels within 70 km of the radar, a bearing between 0 and 180 degrees true relative to the SEAB radar and the radial velocity could not be between 0.5 m/s. The radial velocity check eliminates any vessels that are stationary that the radar is not capable of detecting. The range and bearing plot eliminates any vessels that are in Raritan Bay or behind the radar. Again the radar would not be able to detect these vessels.

That left 54 vessels in the AIS record that the radar could detect. We were able to detect each of these vessels. The range of detection varied between 12 and 61 km from the SEAB radar. Figure 19 shows density maps for our AIS detection on the left and for the HF radar detections on the right. The radar at SEAB is able to show the high number of vessels located outside the entrance to Ambrose channel. The radar is also able to show the high vessel traffic in the Barnegat to Ambrose, Ambrose to Barnegat and the Ambrose to Hudson Canyon. Figure 20 shows the detections for one of the vessels from both the SEAB and the BELM radar site.

Figure 19: AIS density map for June 11, 2012 (left) and HF radar detection density map for the same day.

Figure 20: Detections of the Calusa Coast from 11:00 till 13:38 GMT on June 11, 2012. The red circles denote the detections from the SEAB radar and the blue circles denote detections from the BELM radar. The GPS track of the vessel is shown as the thick black line.

5. Maintain vessel detection data stream access to Open Mongoose

A continuous stream of data is essential to building up statistics on radar detections compared to AIS. The vessel detection data from Sea Bright is archived at Rutgers starting on October 1, 2012. We added a second real time vessel detection license at Belmar in February 2012. All of this data has been sent to the Naval Research Laboratory. We have conducted monthly calls with Dan Newton on the progress of getting the vessel detection data into Open Mongoose. Dan presented a paper at the Tri Services radar conference in Boulder, CO.

Rutgers also had 4 tasks with the University of Puerto Rico. The results of that collaboration are presented here.

1. Advise partners in Puerto Rico on operation of HF Radar Network

Rutgers personnel made two trips to Puerto Rico in November 2011 and March 2012 to assist Puerto Rico personnel in the operation of the radars. The national network metrics

51 for the radars are shown in Figure 21 and Figure 22. The performance of the radar at Cabo Rojo increased after our visit in November 2011.

Figure 21: Screen shot of the diagnostic page from the Fura Anasco 13 MHz radar. The radar provided 82% data return from July 1, 2011 to June 30, 2012.

Figure 22: Screen shot of the diagnostic page from the Cabo Rojo 13 MHz radar. The radar provided 58% data return from July 1, 2011 to June 30, 2012. 2. Define vessel detection test period based on AIS data and visual inspection of spectra

The test period was defined as November – December 2011.

3. Collect range data, deliver to Rutgers for analysis

52 Range data from FURA and CDDO was delivered to Rutgers. It covered the time periods of October 2011 to March 2012.

4. Rutgers analyzes range data for vessel detections

Caribbean testbed site statistics were calculated. Rutgers was able to detect vessels out to 60 km from the CDDO site over a four-hour period on December 1, 2011 (Figure 23).

Figure 23: Vessel detection data from CDDO from 01:00 to 05:00 on December 1, 2011.

53 Outcomes

1. Resources Leveraged

Admin Assistance, electronic data processing, web Mid Atlantic Regional Coastal support, technical support for the 36 site, operational, Ocean Observing System regional HF Radar network NOAA IOOS Program CODAR loan brokering, data coordination with the U.S. National Surface Current National Network, operating frequency permissions Mapping Plan UCSD Coastal Observing Web hosting, Data quality control for the national HF Research and Development Radar network. Center 13 MHz SeaSonde deployed in NJ, 13 MHz bistatic NAVSEA LEAP Program transmitter deployed on shore and at sea Additional support for vessel tracking test in Norfolk to CIT evaluate the system for a Navy port. Development and demonstration of real-time bistatic DHS BOA- software for vessel detection.

Proposal approved to Install Four 13 MHz CODAR NJ Board of Public Utilities - SeaSondes along the south Jersey coast, extending the Funded range of the NY Harbor testbed.

2. Publications

Our collaboration with the Naval Research Laboratory has also yielded a publication.

Newton, D. "Observations and Analysis of the Target Detection Performance of the CODAR HF Surface Wave Radar," The Proceedings of the 2012 MSS Tri-Service Radar Conference, Boulder, CO; 18-22 June 2012.

3. Education

The funding through the COE has contributed to the educational activities of the Rutgers University Coastal Ocean Observation Laboratory (RUCOOL). Error! Reference source not found. Figure 24 shows the number of undergraduate students in the Ocean Observatory Research class. It has increased each year. The breakdown is approximately 50/50 male/female for the class.

Two students from Rutgers are currently enrolled in COE Masters programs. Danielle Holden remained internally at CSR and Blake Cignarella went externally to CREATE. Also, three Rutgers students are employed by RUCOOL participating in COE exercises.

54 /0)-*"/1+).2-&#.3)+"4#'.+)" *!" )!" (!" '!" &!" %!" $!" !"#$"%&'()*&+" #!" !" $!!(" $!!)" $!!*" $!!+" $!#!" $!##" $!#$" $!#%" ,)-.+" Figure 24: Number of undergraduate students in the Ocean Observatory Research class.

Task 1.2, Project 2, University of Puerto Rico Mayaguez (Dr. Jorge E. Corredor, PI)

Objectives and Significance to Stakeholders

The primary objective of the year 4 UPRM HF Radar work plan was the maintenance and continued operation of the HF Radar network on the Mona Passage and provision of local support for the coordinated spiral development cycle in CSR’s tropical HF Radar testbed in Puerto Rico that includes: · Maintenance of the 2 CODAR units on loan currently emplaced at CDDO and FURA · Maintenance of the bistatic Tx station at MCSB · Maintenance of the AIS at Magueyes Island and Mayaguez campus · Continued engagement with FURA and USCG · support to Rutgers in monostatic vessel detection tests with the two- shore based Radars already in operation · support to Rutgers in tests of the reconfigured bistatic transmitter running on shore power on shore in Puerto Rico · Implementation of SLMDB buoy tracking experiment · Maintainance of the AIS page (“Ship Tracker”) and · Maintain a dynamically updated map of surface currents in the Mona Passage

Milestones Met 1. Operate 13 MHz shore based network with 80% coverage statistics (Coast Guard recommended) 2. Operate AIS receiver at UPRM field station and report on vessel detection test 3. With CaricOOS support, maintain operational ShipTracker and surface current maps in the CariCOOS web page 4. Collect range data, deliver to Rutgers for analysis

As proposed, the Mojacasabe bistatic Tx operated uninterrupted and AIS service was maintained throughout the reporting period. Milestones 1 and 2 were met.

Antenna pattern measurements were performed at CDDO aboard UPRM’s R/V TIBURON 11-12 November 2011 and at FURA March 23-24. Radials from CDDO are now being computed with new measured pattern. AIS emplacements and Shiptracker page were maintained operational throughout the year.

Despite these successes, CDDO and FURA stations functioned only intermittently during the course of the year so that posting of dynamically upgraded surface current visualization maps was intermittent as well. Thus milestone 3 was not achieved for long- duration periods. These failures were in large part attributable to equipment age. During the year, we had one UPS failure, a hard drive crash (panic attack), one failure due to obsolete software and one keyboard failure attributable to old batteries. These failures caused substantial down time and necessitated the purchase of new units, including one complete computer (MacMini) purchased directly from CODAR and configured specifically for CODAR operation. At other times, new Mac software (OSX) installations were required.

Natural events also caused significant downtime. Rx cables at FURA were destroyed by a heavy surf event necessitating purchase of new wires and causing 25 days downtime in November 2011. One further cause for non-performance was a determined denial-of- service attack which used up the bulk of the monthly bandwidth allowance at CDDO. AT&T, the telemetry supplier, was prevailed upon to change our static IP address for FURA after 3 such events.

During the performance period, we sought to complete the SLMDB buoy tracking experiment proposed to be implemented and our collaborator, the US Coast Guard was prepared to implement the deployments during passage of one of their cutters through the Mona Passage. However, repeated equipment failures impeded the exercise. This exercise was postponed until year 5.

Data collected as part of milestone 4 was delivered to Rutgers and analyzed in their report (see previous section). A radar range of 60 km was demonstrated and sustained. Consistent radar range achievement is a significant metric of success.

Outcomes

1. Resources Leveraged

Rutgers University – Coastal Ocean Technical support in HF Radar operation Observing Laboratory and maintenance 12 MHz CODAR system loan Bistatic transmitter

Caribbean Integrated Coastal Ocean Admin Assistance, electronic data Observing System - CarICOOS (NOAA processing, web support, technical support sponsored) for the 3 site, operational, regional HF Radar network

Texas A&M University 12 MHz CODAR system loan

Fuerza Unida de Rápida Acción – CODAR site Policía de Puerto Rico

Club Deportivo del Oeste, Inc CODAR site Joyuda, Cabo Rojo

Mojacasabe Beach Resort Bistatic Transmitter site

Task 1.3 Nearshore and Harbor Maritime Domain Awareness Via Layered Technologies

Task 1.3, Stevens Institute of Technology (A. Sutin, PI) TRL 7

Project Objectives and Significance to Stakeholders

The acoustic component of the CSR research is aimed at the application of passive acoustic methods for surface and underwater target detection, classification and tracking. The ability to safeguard domestic shipping and waterside facilities from threats associated with surface and underwater vessels and divers is critical to ensuring a viable Maritime Domain Awareness. Role of acoustic in CSR layered approach utilizing above water and underwater surveillance techniques can be formulated as following:

• Acoustics is the only tool that provides detection of underwater threats. • Acoustic can provide relievable classification of surface boats that other methods cannot. • Passive acoustic technologies are less expensive than other technologies and they are simple to deploy.

The drawbacks of acoustic methods are typically connected with limited detection distances that usually are less than radar detection distances. Also the detection, tracking and classification distances of passive acoustic system strong depend on ambient acoustic noise and acoustic propagation.

The CSR work is aimed at investigation of acoustic signatures of various targets, acoustic noise and sound propagation in various areas. These research form basis for development of methods for improving of detection, tracking and classification of surface and underwater targets by passive acoustic technology. Stevens has built several systems based of collected knowledge and conduced large set of feasibility tests.

During Year 4 researchers from Stevens continued to gather information regarding the characteristics and variability of acoustic signatures from surface vessels under a variety of ocean and weather conditions. These measurements form an invaluable database for use in the characterization of threat signatures, and eventually in decision-support algorithms.

The design of the Stevens passive acoustic sensor system was refined, with the aim of enhancing the detection range, spatial resolution and accuracy of classification. New generation of Stevens Passive Acoustic Detection System (SPADES 2) and several portable recording buoys were developed and built. The portable recording buoys were effectively applied for 24/7 acoustic recording in San Diego area.

Specific Objectives

1. Re-engineer and improve the passive acoustic sensor system, including portability and data display.

The team developed a highly-portable, easily-deployable version of the Stevens passive acoustic system, with anticipated performance characteristics. This represents a system evolution on the path to commercialization and is an important metric for success. Two nodes of the new low weight Stevens Passive Acoustic Detection System (SPADES-2) were developed and built. The first test of the new system in San Diego area demonstrated its effectiveness for small boat detection, tracking and classification. Several portable acoustic recording buoys were built and we applied for 24/7 underwater sound recording in San Diego area.

2. Consider various methods of optimization of vessel acoustic detection systems based on collected vessel signatures, ambient noise, and acoustic characteristics.

The software for ship detection and tracking based of the correlation of signal coming to the each node of the Stevens Passive Acoustic Detection System was developed and triangulation of the lines of bearing from two nodes was developed. The boat localization was demonstrated in the tests conducted in Lake Hopatcong and in San Diego area. The novel lock in frequency algorithm was developed that allowed increasing of the detection range and automatic target detection.

3. Conduct measurements and analysis of acoustic signatures of surface and underwater vessels in the Hudson River for various environmental conditions

A large amount of acoustic signatures of surface boats was recorded in the Hudson River, Lake Hopatcong and in San Diego area. Tests in the controlled conditions of Lake Hopatcong allowed estimation of absolute sound level radiated by a small boat recalculated to 1 m from the boat.

4. Conduct measurements and analysis of background ambient noise in various areas and environmental conditions.

Measurements of ambient noise were conducted and analyzed in various areas of the Hudson River and NY/NJ harbor.

5. Develop vessel classification methods using the library of acoustic signatures collected by Stevens.

Various simplified approaches to acoustic vessel classification including Decision Tree and Machine Learning were considered for limited sets of acoustic signatures. The suggested methods will be extended to a larger set of acoustic signatures in Year 5 of the 60

project. This is an important area that will take consolidated effort. Metrics for success include being able to differentiate reliably classes of vessels from “blind” datasets.

The developed algorithms and prototypes have demonstrated fast data transmission to operational decision-support systems for providing near real-time, detection, characterization, and tracking capabilities in near-shore, harbor and inland waterway environments.

Milestones Met

1. Hardware of new acoustic systems and their deployment: Passive Acoustic Detection System (SPADES-2)

In the Year 4 of the project the new low weight Stevens Passive Acoustic Detection System (SPADES-2) was improved and the second node of the system was built. This second node provides Target of Interest (TOI) localization that cannot be done with single node used earlier. Also several Portable Underwater Sound Recorders (PUSR) were built and applied for 24/7 acoustic surveillance.

The Stevens Passive Acoustic Detection System (SPADES-2) divided to two parts underwater equipment and shore equipment. The underwater equipment consists of a set of 4 hydrophones (ITC model 6050C) arranged on a collapsible aluminum frame. A data hub is centered in this frame. Three of the four hydrophones are joined to the cylindrical hubs through three ~2.4 m collapsible extruded aluminum legs arranged with 120° between each leg. All the hydrophones are connected to the central hub with BH4M Subconn wet-mateable connectors. The hydrophones and supporting frame has a weight in air of ~45 lbs.

Figure 1. SPADES-2 pictures made during the system deployment.

The data hub (see Figure 2) housed: • custom made 4-channel signal conditioner, • USB 2.0 Data Acquisition Board DT9816 , • USB 2.0 to fiber media converter ICRON USB Ranger 2224. • Custom made DC-DC converter to supply power for components.

The mooring has a diameter 8 inches and is 13 inches long. The weight is about 25 pounds.

4- Channel 16 –bit USB to Signal USB Fiber Conditioner DAQ Optics Converter

DC-DC Converter

Figure 2. The schema of the underwater mooring of the SPADES-2.

The underwater equipment is connected to the shore equipment using a combined fiber- optic/electrical cable. Necessary power and data transmission between the underwater nodes and the surface are handles through a MacArtney Underwater Technology type 3444 electro/optical cable. The data is handled using 4 multimode optical fibers 50/125 µm. Two of the fivers are used for data transmission , third is used for a on pulse per/second (PPS) signal from the GPS unit for precise signal timing and a fourth fiber is reserved The power is handled using 2 pairs of two 1.0 mm2 stranded tinned copper conductor insulated with polyofin supplying ~ 24V dc, 2 amps. The overall length of the electro/optic cable is 250 meters.

The dry end of the cable connected to a data storage computer. This part of shore equipment contains not only a computer itself but also Fiber Optics to USB 2.0 converter, GPS receiver and is enclosed in weatherproof Pelican briefcase (see Figure 3).

63 GPS

Fiber optics to Data Storage USB Converter Computer

Removable Spare Hard Drive Removable Hard Drive

Figure 3. Shore Data Storage Computer

The data storage computer is equipped with two high capacity, 4TB or more, removable hard drives. One of them is used to record the data stream received from underwater equipment while the second one is a reserve. If the first hard drive is full, the reserve hard drive takes over its function.

Another duty of the data storage computer is to transfer all data to a signal processing computer. This computer is based on Intel I7 3.0 GHz processor and is able to perform all operations like FFT transform, frequency domain filtering, multi-pair cross-correlation, target tracking and visualization.

The schema of the SPADES deployment is presented in Figure 4.

Figure 4. Stevens San Diego Experiment Acoustic System Deployment Schematic

The research vessel was anchored in position using a three-point anchoring method that can be considered to be a modified Bahamian anchoring scheme. Using this method, three pyramid-shaped ~250 lb. cast iron anchors were placed on the perimeter of a circle with a radius equal to the depth of the water (~23 m). Each of these anchors has 25 m of ½” steel chain that lay on the seafloor. The 1/2'” chain lead to ~15 meters of 3/8” chain which will lead to ~ 15 meters of 3/8” spectra line terminating at an ~1 m diameter USCG regulation surface buoy. The nature of this anchoring scheme reduced to a negligible degree any dragging of the anchor chain on the sea bottom typically associated with wind or surface current directional variation. This set up also allowed for the research vessel to swing with wind and tide while still allowing for connection to the sensor nodes power/data cables without concern for fouling the data cable or dragging the nodes.

Portable Underwater Sound Recorder

During the San Diego test the Portable Underwater Sound Recorder (PUSR) was used in addition to two permanently deployed SPADES systems. This inexpensive device provided the opportunity to record underwater sound almost continuously with short breaks for digital recorder replacement. The most important advantage of using the Portable Underwater Sound Recorder is to record signals overnight when SPADES systems do not operate. 65

The current version of PUSR is based on TASCAM DR-05 Linear PCM Recorder.

Figure 5. TASCAM DR-05 Linear PCM Recorder

This device is capable to record two channels of signals with resolution 16 bit or 24bit and a choice of 44.1 kHz, 48kHz and 96 kHz sampling rates, uncompressed WAV or compressed 32-320kbps MP3 formats onto a replaceable microSD flash card.

Using a 32GB microSDHC card allows over 30 hours of recording time at 24-bit/44.1 kHz. For MP3 format recording time may reach 300 hours. DR-05 is a low power device, so two AA size battery allows about 8 hours of operation.

To build an underwater sound recorder, two hydrophones were installed instead of microphones. For using in this design a low cost AQUARIAN H2a hydrophone was chosen. It provides a sensitivity of -180dB re 1V/uPa in frequency range 20Hz-4kHz .

Also additional high capacity D cell batteries were installed to prolonging recording time. The recorder and the batteries were enclosed to waterproof OtterBox 3250 case . .The OtterBox3250 Series designed to withstand submersions up to 100 feet.

The fully assembled Portable Underwater Sound Recorder shown in Figure 6. The overall weight of this small system does not exceed 3 lb.

Figure 6. Portable Underwater Sound Recorder

66 Schematic of the PUSR deployment setup shown in Figure 7 below.

Figure 7 . PUSR deployment setup

OtterBox with Digital Recorder DR-05 inside installed on a rope by using zip ties. Above and below Hydrophone1 and Hydrophone2 are installed using the same method. Each hydrophone equipped by 5 ft long cable, so distance between hydrophones may be chosen in range up to 10 ft. On the top part of rope we installed a small Buoy for developing pulling force to help to keep the recorder and hydrophone assembly in a vertical position. The length of rope may be adjusted if a certain depth of installation is needed. The lower end of the rope attached to Anchor 1 and rested on the sea bottom. The second rope 150 ft long is used to connect Anchor 1 and Anchor 2. The Marker Buoy attached to Anchor 2 permits finding the exact position of deployment PUSR and later to retrieve the system from the water. The selected two anchors setup effectively eliminates additional noise developed by the Marker Buoy by interaction with surface waves.

The advantages of the presented portable Underwater Sound Recorder are: • Small size; • Light weight; • Easy deployment and recovery; • Long recording time without battery replacement; • Low cost; • Autonomous

This kind of device is very suitable for long-term research of marine life, ambient and shipping noise in ports, rivers, bays and coastal area.

The disadvantage is the inability to provide signals for real time processing, because there is no communication link with the surface or from the shore.

2. Signal processing and software for target detection and tracking

A single Stevens Passive Acoustic Detection System (SPADES 2) provides passive acoustic detection and line of bearing. For target localization and tracking two or more such systems are required. The test with two systems was conducted in April 2012 in Southern California. In this experiment Stevens deployed two SPADES passive acoustic detection systems, separated by ~300 meters. Each system is able to determine bearing to the source of the acoustic signal and find the azimuth estimate towards the target. By intersecting the bearings, the two systems are able to estimate target location. Those intersections are tracked by a linear Kalman filter using multiple hypotheses tracking (MHT), which allows multiple targets to be tracked simultaneously. Those tracks can be overlaid on a map as shown in Figure 8.

System deployment Determined bearings Target 2

System 1

System 2 290m

System 2 System 1

Target 1

Figure 8. Two targets are localized and tracked

Each track can be assigned a unique ID number, also a Kalman filter model can be used to determine parameters of the target and generate a contact report. Field Description ID Unique identifier of the track Georeferenced target location Latitude, Longitude Speed over ground Speed of the target

Course over ground Angle relative to true North, at which the target is traveling Table 4. Contact report fields

Signal processing and software for determination of line of bearing

The software developed by Stevens was used for TOI detection, classification and tracking. The joint signal processing between signals from various sensors provides acoustic source localization. The main method for acoustic source detection and bearing determination is based on the calculation of cross-correlation of acoustic signals recorded by various pairs of hydrophones.

Let us consider the signals received by two hydrophones separated by a distance L . They record the noise radiated by a source whose direction makes an angle with the normal to the line between the hydrophones. The distance between the source and the hydrophones is much larger than L. An example of such a configuration is illustrated in Figure 9. The noise radiated from the ship reaches the two hydrophones with a delay !T (also called TDOA or Time Difference of Arrival) between them: Lsin! "T = c (1) where c is the speed of sound in air.

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Figure 9. Separated hydrophones and TDOA in cross-correlation Let us assume that a single target contributes to the acoustic field and that the signals xt() and yt() recorded by the two hydrophones are delayed versions of the same signal: yt()= xt (!" T ) (2) and !T is the delay introduced in Eq. (1) The cross-correlation of the signals is defined as:

69 ∞ (3) Rxtytdtxy ()ττ= ()(ʹʹʹ− ) ∫−∞ For two delayed signals of the form (4), RRT() ( ) (4) xy ττ=xx −Δ R () The cross-correlation xy τ of the signals from the two hydrophones is the same as the R () autocorrelation xx τ of the signal from the hydrophone 1 but shifted to the time by τ =ΔT that corresponds to time difference of arrival (TDOA). Because the autocorrelation of a signal is maximum at τ = 0, the cross-correlation is maximum at τ =ΔT . This means that time location of the maximum of the cross-correlation can be used to estimate the angle of arrival of the acoustic waves emitted by the target.

The above angle corresponds to the angle of arrival, it should be noted that this angle lies within the plane that contains the two sensors and the target, theoretically this plane has a degree of freedom, as it can rotate about the axis connecting the hydrophones, thus it defines in a cone of possible target locations, with side angle:

A simple approach where only one pair of cross-correlation is used produces ambiguity as the same delay corresponds to two possible bearings of source, symmetrical along the line connecting the sensors in pair. This ambiguity can be resolved by using multiple hydrophone pairs.

Another limitation is that the angle resolution is not uniform and declines toward the direction of the line connecting the hydrophones. As it is seen from Eq.(1) the dependence of angle from delay is non-linear and as such the accuracy of the angle determined by one cross-correlation pair varies with the angle, as can be seen by computing the rate of angle change per unit delay: dcα dcLsin−1 ( τ ) == ddττ 2 ⎛⎞cτ (5) L 1−⎜⎟ ⎝⎠L

The limitations of using a single cross-correlation pair to estimate bearing using angle of arrival computer from TDOA can be overcome by processing all the array elements simultaneously. For example, in the SPADES sensor configuration shown in Figure 10 we see cross-correlation pairs that can be formed using multiple sensors depicted as differently colored lines, connecting sensors. All of those cross-correlation pairs are differently oriented, therefore the low-resolution zones for the angle will correspond to different bearings, and also the ambiguous bearings from different cross-correlations will

correspond to different bearings, while true bearings will match in all cross-correlations.

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$%()&))! +&&*(&,-,! +&&*(&,---! +&&*(&,--)! +&&*(&,--.! Figure 10. Sample locations of sensors, color-coded lines show! possible pairs To illustrate the advantage of using multiple sensors, the sensitivity TDOA to bearing change with delay change for various pairs of cross-correlation is plotted against bearing in Figure 11. We can see that nearly uniform resolution can be achieved by picking the pair with the best resolution for each bearing to estimate the angle. To acquire the measurements of target positions, peaks are detected in multiple cross-correlations.

Figure 11. Sample relative angle resolution, color-coded lines show resolution metric for individual pairs. Black envelope shows potentially achievable resolution when picking the best pair.

71 We can define a simple criterion for having a contact at a certain bearing: for a certain angle. If there is a source at a given bearing from the system, then cross-correlation results from all (or at least majority of the pairs) are likely to have a peak at a delay that would correspond to a sound wave incident from that angle. Since the TDOA estimates are not exact, to declare that the peaks are present at a lag consistent with an arrival from a certain bearing we can set a small tolerance for possible discrepancy in delay. This way, the algorithm to find contacts can first form a list of hypothetical angles at which the contact can be present then test them against the detected peaks in cross-correlation.

To form the best-possible hypotheses, the algorithm will go through lists of detected peaks for all cross-correlation pairs and process only the detections which are at the angle, where the pair that yielded the peak is the one which has maximum resolution of all cross-correlation pairs. This can be made through a lookup table, where pair indices are sorted by descending resolution versus angle.

Note that every delay corresponds to 2 possible bearings of arrival, which means every delay has to be resolved into both possibilities, and tentative clustering performed, then the one which had the worse cluster (fewer matches, more spread) is discarded. After that, the matching delays are removed from the list.

Both the angle and mirror will have similar resolution within this cross-correlation; however, this may not mean that for both bearings the cross-correlation pair that yielded the angle estimates has the best coverage. So if one of the angles is not the best resolution match, this means that it can be disregarded for now - it will be picked up when another pair, which has best coverage is processed. The hypotheses are tested using the criterion described above and a contact is declared only if it holds for a given hypothesis.

An example of the SPADES display with the result of this algorithm is shown in Figure 12. The angle history plot consists of a radial display with the radius corresponding to time; inner radius represents time in the past, where the outer radius is the latest measurement. Angle referenced to true North, with 0 degrees represented as direction up and clockwise represents easterly rotation. The resolved true bearing is plotted as thick yellow line, while the bearings corresponding to all detected peaks in cross-correlation are shown as thin lines with colors representing different cross-correlation pairs. Note that the red graph is very discontinuous, showing the angle granularity due to low resolution near the line connecting the two sensors in the pair, while the blue graph is continuous and smooth, as the pair it corresponds to it is nearly orthogonal to the one producing red graph. The algorithm would rely on the pair providing blue graph to produce best possible hypothesis for bearing and determine if there is a corresponding peak in the pair producing the red graph.

North

Latest Ambiguous True bearing reading bearings filtered (outer circle)

West East

Oldest reading (inner circle)

South

Azimuth angle history over time

Figure 12. SPADES display showing resolved unambiguous bearing.

Target localization

The acquired bearings form rays emanating from the centers of cross-correlation pairs are used to acquire those bearings. The location of the source can be found by simply intersecting those rays to produce a measurement of the target position.

Position determination demands that two systems can determine the angle simultaneously; this condition persists for several consecutive scans. The geometry of using only two systems imposes limitations due to different sensitivity of results of bearing intersection location precision depending on the relative position of the resulting bearing intersection.

The tracking algorithm is currently based on linear Kalman filter and multiple hypothesis tracking. Kalman filter is a mathematical method, which allows us to use inexact observations of target position to estimate and predict target’s position using a linear 73

model of target motion over time. In our case, the behavior of each target is modeled by a simple 2D kinematic model of an object on a frictionless surface maintaining constant speed, but we allow it to undergo random acceleration by assuming that the state knowledge is not exact. Such model can be simply expressed in linear algebra, which is a requirement for Kalman filter implementation.

Existing models Model association w/measurement KF Updat e count=count+1 Me asure me nts Via associated Min(Mah alan ob is) Output Models with Existing models Co un t> thr esh old w/ o m e asur em e nt count=count-1 Mo de ls associated from previous KF Predict KF Predict Measurements step w/o models KF Init associated KF Init

Figure 13. Kalman filter step executed every time a new scan arrives.

To use an observation to update Kalman filter, this requires knowledge of covariance of the Gaussian distribution associated with the measurement. Due to the uncertain nature of the angle determination, the resulting intersections are also uncertain. If we assume that the angles have a fixed standard deviation of error that is uniform across angles, we can determine the uncertainty of the resulting intersection. Since we are using linear Kalman filter, we have to model the uncertainty with bivariate Gaussian distribution that can be characterized by a mean and covariance matrix. Since our location of the detection is an intersection of two lines, the distribution will vary depending at which angle the intersection happens and the distribution of the angle measurement error. We can fit a normal distribution that will have maximally close parameters to the one that is actually the result of the error in the angle. To do that, we can randomly perturb the input bearings according to a Gaussian distribution of angle error (Figure 14), and fit a bivariate normal distribution to resulting sample of hypothetical target measurements, the standard deviation of error is adjustable (e.g. in our examples we use a value of 1 degree). To speed up the computation, the covariance of all possible angle combinations are precomputed at the program start and later at runtime are taken from a table.

!" $% $" &% &" % " #% #&" #&% #$" #$% #!" #!" #$% #$" #&% #&" #% " % &" &% $" $% !" '()*+,-./00102.3 Figure 14. Notional position error for intersection of two bearings perturbed by normally-distributed angle error and error ellipse of fitted Gaussian at 2 sigma.

In Figure 15, the error ellipses for all bearing intersections for all possible angle combinations of angles 0-360 in 6 degree increments. We can see that the covariance and the amount of the uncertainty depend on the location. This is because in case where the intersection occurs with sharper angles, small variation of bearing estimate results in large variation of range. Extreme case of this effect is along the line connecting two systems, where the bearing rays coincide and we cannot determine the range using 2 systems and there is no single point of intersection. This illustrates the limitations of using only 2 systems and small baseline. At longer ranges the localization accuracy decreases. Note that those are uncertainties of individual measurements by bearing intersection and by performing weighted averaging Kalman filter is capable of improving on the uncertainties of individual measurements.

Figure 15. Notional position uncertainty ellipses for 1 sigma for intersection all combinations of bearings every 6 degrees.

The overlay presented in Figure 15 provides a visual guide to the uncertainty of the target position. If the intersection of a pair of simultaneous bearing estimates gives us a value, we can draw a 1 sigma ellipse around it, it will encircle 39.4% of all results of 2 normally distributed angle errors with 1 degree standard deviation. If we enlarge the ellipse by a factor of 2.45, it will encircle 95%. This allows us to say that with 95% certainty, the true target position is within the ellipse

Since we can have multiple peaks in cross-correlation, we can have multiple contacts in a scan, thus we need to maintain a number of Kalman filter models -- one for each target. We need to define conditions for creation of new models, associating measurements with the models, destruction of the models when the targets stop producing contacts that can be associated.

The association between measurements and models is done by first performing the prediction step in Kalman filter, thus estimating the new state of the models at the time of measurement. The measurements are associated with the model of which they are most likely to belong or most closely predict the measurement, given the position estimate and the covariance of the position estimate (part the state of the model) and the measurement and its covariance. An efficient way to do it is to use Mahalanobis distance, which increases as probability that the measurement belongs to the model declines.

T T (6) d(X,Y) = (X −Y) (PX − PY ) (X −Y)

Where X is the position estimate from the Kalman filter model after update, Px is the covariance of the position estimate, Y is the measurement, PY is the covariance of the measurement. By setting a threshold on maximum Mahalanobis distance between the estimate and measurement, we are able to implement an efficient gating algorithm, preventing measurements that cannot be plausibly connected with the current tracked target from participating in the update process.

For each measurement, Mahalanobis distance is computed between all the models and the current measurement, if the distance is within a certain chosen threshold, then the measurement is associated with the model, thus only measurements. If no model can be found within a distance smaller than the chosen threshold, then a new model is created. For each model, a counter of updates is kept, it is incremented whenever an update is made and decremented whenever a certain model doesn’t have any detection assigned to it. A contact is only reported when the counter is above certain value, thus for a newly created model to be reported as a contact it takes a number of successive contacts to be associated with it to be reported. If a target which is tracked by a particular Kalman filter model is no longer producing contacts, then the most likely outcome is that the model will fail to associate with any data, thus every scan the counter will be decrements and the model will be discarded when the counter reaches zero. The use of Kalman filter model directly estimates some parameters of moving targets. Tracking parameters that can be acquired in the model are listed in Table 5.

ID Unique identifier of the model Target position Latitude, Longitude Speed over ground Speed of the target Course over ground Angle relative to true North, at which the target is traveling Covariance of target Covariance of the multivariate Gaussian distribution used to position estimate model uncertainty of position estimate Table 5. Model parameters

A result example can be seen on an SPADES display in Figure 16 showing a track of a target 808 m away along with the uncertainty of position depicted by 2 sigma ellipse.

Figure 16. SPADES tracking TOI bearing intersections using Kalman filter showing uncertainty ellipse

Examples of tracking of TOI during the experiment

During the test we had the opportunity to test the performance of the tracker by tracking our targets of interest and comparing the results to ground truth position measurement acquired with GPS tracking devices placed on TOI.

Along with our TOI we have observed some boat traffic that was not participating in runs, the system was able to separate those boats and track them alongside TOI, one of such examples shown in Figure 17, where we have observed a boat at bearing 44 NE from the position of System 1, while TOI Panga was performing a run.

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80 2 continued. Lock in frequency algorithm for acoustic targets detection

Development efforts were first aimed to achieve reliable source detection using acoustic signals from different type of surface and underwater targets and recorded in different environments. Typical acoustic emission for boats contained large components of wide band noise superimposed on boat specific harmonics. The main task for long-range target detection was to identify the frequencies of narrow frequency components (will refer to them as harmonics) as a function of time. For a real time problem, the major limiting factor was the required frequency resolution better than 1 Hz, which implied that at least 1 second long portion of acoustic signal had to be processed.

The main idea behind the harmonic extraction is the scoring system that is applied to track the frequencies over time. The scoring system has been designed to maximize the score for the frequencies that appear on the same track and minimize the score for those peak frequencies that appear at random from the noise. The fundamental frequency and harmonics appear at predictably varying frequencies and accumulate higher score over time, while the noise peaks score reduces to zero over time. Once particular harmonic receives the score above set threshold, the target is considered detected. With multiple harmonics, the target is considered detected if at least one frequency received a score above the set threshold, as described below.

• This research is concerned with a discrete harmonic case of waterborne sound radiation distributed over a broad bandwidth and originating from an unknown position.

• The goal is to detect and classify the source in the presence of environmental noise using limited network of microphones and fast signal processing algorithm. The scope of research is limited to the detection and classification of harmonics. The following basic target detection and classification algorithm requirements were established:

• Must be simple, computationally inexpensive, and easy to implement

• Real time vessel detection: response is less than 2-4 seconds

• Good tolerance to noise

• The processed data can be used as features for Real time automatic classifier

• Compatibility with low cost hardware

• High frequency sound waves attenuate in water more than low frequency sound waves. Consequently, the low frequency sound yields sufficiently strong signal 81

that can be detected over large distances. Thus the bandwidth of interest was limited to a few hundred Hertz where noise from marine environment was also significant. Among other features, a moving vessel generates a vessel specific set of periodic harmonics which map to spectral lines in the frequency domain. The harmonics travel at different speeds and undergo different Doppler shifts as a result of vessel motion. The monitoring of harmonics enables to define both the vessel speed and type recognition. In case of low noise, relative phase difference between multiple harmonics enables the angle of arrival of the vessel emission to be determined.

• An algorithm that keeps track of limited history of identified harmonics and then assigns the individual harmonics score is implemented. The idea behind the program is that maritime vessel harmonics are likely to appear at predefined frequencies and get highest score, while the noise peaks appear at random and will have their score reduced to zero. Once particular spectral line receives the higher score, the target is considered detected. With multiple harmonics the target is considered detected if at least one of the fundamental or harmonics received the score above a set threshold, as described below. For maritime vessels the Doppler shift is small, and a region around identified frequency could be excluded from future consideration.

• The set of algorithm tuning parameters with some numerical values is listed in Table 1. The most important parameter is ta - target detection time in seconds. In real time the signal is processed one sliding window at a time and this parameter translates into a number of consecutive steps that are required for the frequency identification. This parameter defines the detection score threshold required to consider a particular harmonic frequency identified. For example, if the frequency f=150 Hz plus minus allowable small frequency shift at each step has occurred in each consequent time window for a total duration of ta, then it is considered detected.

• Next important parameter is tl – lost target trust time. It defines how long a particular detected frequency is considered after it has been lost. This parameter is quite helpful when working with noisy signals. For example, if the frequency f~150 Hz was detected, and then suddenly disappeared, i.e. was not found in the spectra, the algorithm allows tl time before considering it lost and resetting detection process. When target is lost the respective frequency score is dropped to zero and same frequency must consequently appear for at least another full detection time before it is qualified as detected again. This clear drawback of current algorithm is intended to be compensated by introducing the above lost target trust time. With current set of parameters it implies 1.5 seconds delay in detection while lost target trust time is considerably smaller to avoid false positive interpretation.

• The third important parameter is v - maximum target speed in m/s, which translates into 1± v c Doppler shift related percentage of the frequency change. It was shown above that for a short time the single harmonic frequency change can be between 1 – 2 Hz. These values were used to define tolerance criteria for a frequency mismatch. If the difference between two identified frequencies at two consecutive time windows is greater than this parameter, then they most likely belong to the different harmonics or are produced by noisy signal.

Parameter Comments Value Fs - sampling Hardware parameter: defines Nyquist frequency ~1kHz frequency (Hz tw = sliding window Hardware / software parameter: defines frequency 1 s (s) resolution and system response time to = window overlap Software parameter: defines time resolution and it 0.5 s (s): to < tw is dependent on computer efficiency ta – target detection Target detection time is converted to a number of 1.5 s time consecutive windows for harmonic frequency detection tl– lost target trust Lost target trust time is converted to a number of 1.5 s time consecutive windows for harmonic frequency detection v - maximum target is converted to a maximum allowable frequency 50 m/s speed shift per window step F1 and F2 - defines spectral search window for multiple 10 – 200 frequency of band of harmonics Hz interest Table 3. Maritime vessel detection key parameters

• The pseudo-code of harmonic detection is shown below:

• Compute FFT → Identify new spectral peaks → Match new and old peaks → Increment score for matched peaks → Zero detection score for mismatched peaks, if score is below threshold or lost target trust time expired→Set it to threshold, if score is above threshold → Continue.

• Details of the detection algorithm with elements of classification are schematically depicted on the block diagram as shown in Figure 19. Input signal is processed via FFT and peak frequencies are identified, then they are matched against previous measurements, and the scores are updated. This process continues in a loop while the statistics for target identification are collected in parallel and compared to database. The latter is projected as a next step and current algorithm only collects the information about the vessel’s acoustic signature without storing or matching them against the database.

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Figure 19. Simplified block diagram of algorithm for target detection.

• The detection process can be roughly subdivided in three phases: 1) building phase, 2) locking or training phase, and 3) tracking or classification phase.

1 500

0.5 400

300 0

200 Amplitude (a.u.) Amplitude -0.5 Amplitude (a.u.) Amplitude

100 -1

0 0.2 0.4 0.6 0.8 1 0 Time (s) 100 150 200 250 300 350 400 Frequency (Hz) Figure 20. Windowed signal and the corresponding Fourier spectrum during building phase. The yellow symbols mark identified peaks.

1. Building phase: the input signal is processed by the Fast Fourier Transform and several harmonics peaks are identified in a predefined frequency range, typically between 10 and 200 Hz. The frequencies and amplitudes of the identified peaks are saved into the frequency bin array. The peak identification is illustrated in Figure 20. •

85 2. Locking phase: A set of newly identified frequencies is matched against the set of previously identified frequencies. If the new and old frequencies fall within the same t bin, respective bin frequency score is incremented until the threshold of a ! is t reached. When the threshold of a ! is reached this particular harmonic is considered locked and vessel detected. If at some point in time previously detected harmonic t appear to be missing, the score is kept constant for a period of time l - lost target trust t time, and then the score is reset to zero. If harmonic reappears within l time the process continues as normal. This process is illustrated in Figure 21 for a single harmonic. In short, once frequency gets the highest score, it is considered locked. The range of frequencies corresponding to the maximum Doppler shift around locked harmonics is excluded from further searches. For fast boats it can be up to 10 Hz, for small boats it is in Hz range. Because of the noise about 5 Hz around each identified harmonic was typically excluded.

0*" E3" , Hz %.C' G5DH requency F

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Figure 21. Spectrogram showing the detected harmonic between 10 Hz and 200 Hz. Red dots indicate detected vessel.

• 3. Tracking phase: A separate frequency bin array is created to keep track of harmonics to be classified. Each time there is a match between two consecutive harmonic frequencies the corresponding bin score is incremented, otherwise it is reduced by half. This enables to collect statistics over relatively long duration even if there is a slow drift in harmonics frequency. Ideally this phase is implemented when the source is producing stable acoustic emission so that most of its harmonics could be detected and used for the source classification possibly using a database of known acoustic signatures.

• The graphical user interface developed for boat tracking, detection and phase calculation is shown in Figure 22.

Figure 22. The graphical user interface developed for boat tracking, detection and phase calculation.

3. Acoustic parameters required for estimation of acoustic system performances and detection distances

Passive acoustic systems utilize reception of acoustic signals in noisy environments. The acoustic wave decreases with the distance from the source and cannot be detected when the signal becomes less than ambient noise.

Let consider what acoustic parameters have to be measured for estimation of the detection distances in various environmental conditions. There are three main acoustic parameters in Passive Sonar Equation and we consider this equation according to the book [1]

The source level SL is the acoustic spectral density produced by an acoustic source recalculated to a distance of 1 meter. All units in underwater acoustics are usually presented in dB regarding 1 mPa. SL can also be referred to as the TOI acoustic signature and the goal of our work is the measurements of this parameter for various TOIs.

The signal is measured by hydrophones located at some distance R from the target. The acoustic signal level L received by the hydrophone is less than the SL due to attenuation during sound propagation and can be written in the forms

L = SL - TL (7)

Where transmission loss TL, is a decrease in the sound level radiated by a target over a distance.

The third main parameter is the level of ambient noise – NL (Noise Level)

The estimation of the detection distance can be done on the basis of the passive sonar equation. The simplified version of the passive sonar equation is derived from the condition that the recorded signal is equal to noise L=NL:

SL – TL = NL (8)

All three parameters can be measured independently in various conditions. For example we can measure SL of TOIs in the experimental condition on Lake Hopatcong and TL and NL for environmental conditions in Southern California. It will allow for the application of the results from a shallow fresh water lake to a deep salt water environment with additional sources of noise.

Let consider how we can find these three acoustic parameters in the Hopatcong test. Transmission Loss: TL In the Hopatcong test the Transmission Loss for various distances R is denoted as (TLH(R), (we use subscript “H” for Hopatcong test parameters) was measured by moving the calibrated emitter along the tested acoustic path. This calibrated emitter (Prerecorded Signal Emitter: PSE) has a known Sound Level radiation, SLPSE. We recorded the signal from the PSE: LPSE, that allowed for estimation of the Transmission Loss according to the formula:

TLH(R) = SLPSE - LPSE (9)

Source Level: SL

This parameter is the main parameter that we were going to find in the Hopatcong test. In this test we measured the acoustic signals of TOIs LH (R) depending on the distance from the hydrophone R.

The measured Transmission Loss allows for estimation of the Source Level according to the formula

SL = LH(R) + TLH(R) (10) 88

At the time of this report the signal processing of the recorded data is ongoing and the Source Level of TOIs are presented in the next section "Acoustic signatures and Source Level of several small boats"

.Noise Level: NL

This parameter can be measured experimentally by a single hydrophone. Results of Noise Level measurements for the Hudson River are presented in the section " Ambient Noise" of the report.

3. Acoustic signatures and Source Level of several small boats

Passive underwater acoustic systems do not emit any acoustic signals and only use acoustic energy radiated by target of interest for detection. As a result it is very important to know target source level for different types of targets of interest. Wе have conducted a set of test for measurements of various small boat Sound Level (SL) of boat acoustic signatures in controlled condition on the Lake Hopatcong.

During the Hopatcong test the acoustic signatures of six various boats were measured and we present two of them in this report:

TOI 1 is a high-speed (~50 knots) 36’ planing hull with 675 installed HP in 3 outboard motors (Figure 23).

Figure 23. TOI 1 high speed boat with 3 engines. TOI 2 is a Panga vessel with Yamaha 200 HP 2-stroke outboard engine. Maximal speed was 37 knots.

Figure 3.3.

Figure 24. TOI 2Panga

90 To acquire the acoustical noises emitted from these boats, the Stevens passive acoustic detection system (SPADES) was deployed about 100 meters away from the shore. On Figure 25 shown below this system is depicted as small red circles. In the described setup the hydrophones were placed along an approximate line at unequal distances from each other, in particular two of the hydrophones were set approximately 1 meter apart, which in the scale of the figure almost brings them in to one circle.

Figure 25. System of hydrophones SPADES and boat path recorded by GPS. The white line on figure 25 shows the GPS track of one of the actual runs.

For measurements of the TOI sound level we used comparison of the TOI SL with the acoustic signal produced by calibrated speaker from the same point. The schema of the SL measurements is shown in Figure 26.

SPADES Target of Interest Sound Propagation Path

Calibrated speaker SPADES Sound Propagation Path

Figure 26. Schema of SL measurements by comparison with the calibrated emitter.

SPADES is equipped by calibrated hydrophones TC6050C for which the sensitivity is known and the gain and frequency response of signal conditioner is also calibrated.

Acoustic signal level received by a hydrophone L depends from the target Source Level (SL) and transmission loss (TL) on sound propagation path.

L=SL-TL (11) Hence SL=L+TL.

As shown above L may be found by direct measurement. TL is an unknown value but may be measured if calibrated source of sound (Prerecorded Signal Emitter: PSE) is used instead of target of interest, because

TL =SLPSE-LPSE . (12) Here SLPSE is Source Level of the reference calibrated source.

The estimations of SL were made for separation between SPADES and target as 200m. As a calibrated source of sound we used underwater speaker Lubell LL916. Transmit voltage response vs. frequency for LL916 shown below in 27.

92 Figure 27. Lubell LL916 transmit voltage response vs. frequency from speaker documentation.

Hz Pa

µ , dB re 1 L

Figure 28. Level of acoustic signal LPSE . measured by the SPADES at 200 m from the calibrated source.

93 Excitation signal was multi-sine with no equidistant frequencies, starting with the frequency 222 Hz and 1.41 Vrms level for each component. For analysis we have chosen Channel 0 and Channel 1 of SPADES. Graph of SPL developed on the hydrophones is shown in Figure 28.

The results of measurements of Source Level for various speed and load are shown in Figures 29- 35 below.

m Hz

Pa µ

1 re dB , SL

Figure 29. TOI 1, Speed Boat, Run , speed 10 knots

m Hz

Pa µ

1 re dB , SL

Figure 30. TOI 1, Speed Boat, Run 2, speed 25 knots

m Hz

Pa µ

1 re dB , SL

Figure 31. TOI 1, Speed Boat, Run 4, High-speed

95 Figure 9.6: TOI 1, White Whale, Run 4, 2011-04-26, High-speed

m Hz

Pa µ

1 re dB , SL

Figure 32. TOI 2, Panga, Run 21, speed -8 knots

Hz Hz

µ Pa

µ 1 re dB dB re 1 re dB

, , L SL S

Figure 33. TOI 2, Panga, Run 22, speed -22 knots

m Hz

Pa µ

1 re dB , SL

Figure 34.TOI 2, Panga, Run 33, speed -9 knots, Heavy loaded

m Hz

Pa µ

1 re dB , SL

Figure 35. TOI 2, Panga, Run 22, speed -22 knots

4. Ambient Acoustic Noise in the Hudson River

Ambient noise measurements were conducted along the Hudson River from Upper Nyack to Castle Point. Noise was measured at 20 points of the Hudson River. The points of measurements were separated by distance of 1 mile. Tests were conducted from the Stevens research vehicle Savitsky that deployed an ITC-6050 hydrophone at the depth of 3 feet. Acoustic signals were recorded by the Boat Computer, which used three channels with different frequency filters. In these analyses, the wide band filter with the flat frequency response was utilized. The noise spectral density was calculated taking into account the frequency response of the hydrophone.

Data set# Site Description NL21 1 mile above Tappan Zee Bridge (TZB) NL22 Drift under TZB NL23 1 mile below TZB 2 miles below TZB. Close to left bank and rail NL24 road. NL25 3 miles below TZB. Middle of the river NL26 4 miles below TZB. Middle of the river NL27 5 miles below TZB. Middle of the river 6 miles below TZB. Middle of the river. NL28 Vicinity of the city. NL29 7 miles below TZB. Middle of the river NL30 8 miles below TZB. Middle of the river NL31 9 miles below TZB. Middle of the river 10 miles below TZB. Drift close to Washington NL32 Bridge NL33 Drift under Washington Bridge (WB) NL34 1 mile below WB NL35 1 miles below WB NL36 2 miles below WB NL37 3 miles below WB 4 miles below WB. Choppers and ferry. Noise NL38 level corresponding to a passing ferry. NL39 Castle Point Hydrophone in air. Hydrophone noise NL40 measurements. Tabl.13. Position of points of noise measurements.

Several pictures of measurements sites are presented in Figure 36.

Figure 36. Selected pictures of measurement sites.

Figure 37 presents a sample of the ambient noise spectral density in various points. High noise levels were observed around bridge, tunnel, and bank. They are 30 dB higher than

the typical ambient noise level in the middle of the river in the frequency range of 20 kHz – 60 kHz.

Figure 37. Ambient acoustic noise spectral density in various areas of the Hudson River

5. Acoustic ship classification

Current state of art in acoustic ship classification

The mail goals of passive acoustic methods are detection, tracking and classification of surface and underwater targets. Classification is one of the main advantages of acoustic methods that can provide much more reliable classification in comparison with radar, optical or satellite methods.

All vessels underway produce broadband and discrete noise that is a combination of hydrodynamic noise and noise generated by the ship’s crew and the operation of onboard machinery. The low-frequency underwater acoustic output generated by surface ships is a significant contributor to background ambient noise in the open sea and in littoral

100 regions, near harbors and shipping lanes. With minimal acoustic attenuation at low frequencies, ship acoustic footprints extend over tens to hundreds of kilometers.

The underwater acoustic output from a moving ship is called its hydroacoustic signature. As a result of a number of studies since the World War II, the general form of a ship’s signature are reasonably well known. Physics of Sound in the Sea, the World War II compendium [2], describes the radiated noise of many ships of that period, as summarized in a textbook by Urick [1].The existing methods concerning ships detection and classification by means of their acoustic footprint are reviewed elsewhere [3-7].

The ship radiated noise originates in different sections. The propeller is the principal noise source in the stern, producing strong continuous spectrum in the frequency range of 1-5 kHz. Between the stern and the midway, ship noise is dominated by the propulsion machinery producing 10-100 Hz strong line components. In the midway, noise is generated by auxiliary machineries in the frequency range of 100 Hz - 1 kHz. In general, two major noise components are recognized [6]:

1. Narrowband propeller and machinery sounds: Fundamental propeller shaft and blade-rate signals lie in the 1–20 Hz range, dependent on ship and propeller design and speed, with multiple harmonics extending upwards to 100 Hz [7]. Other narrowband shipboard machinery signals extend as high as 3 kHz. Blade rate source level is reasonably understood and agrees reasonably well with a model estimates [8].

2. Broadband cavitation, turbulent flow, and bubble generation sounds: Signals extend from roughly 100 Hz upwards, with a spectral level varying approximately as inverse frequency squared. The radiated noise data show high-level tonal frequencies from the ship’s generator and blade rate harmonics due to propeller cavitation [9,10 ].

Classification of ship-radiated noise has attracted a lot of attention in the past few decades. Several factors make this aim difficult to accomplish: complex sources that induce noise of a moving ship, strong disturbance from ambient noise, surface and bottom reverberation effects, and lack of any a priori knowledge of the observed targets. Investigation of surface ship acoustic signatures with particular examination of its speed, size, directionality and the effects of maneuvers produced results that may enable ship classification and detection.

The acoustic signature of the ship moving at constant speed was found to agree closely with empirical relations [11]. Specifically, above 150 Hz, the ship noise spectral level estimates exhibit close to an inverse frequency-squared dependence, in general agreement with model. The broadband spectral source level shows a dependence approximately as a ship speed to the power of 4, below 6 m/s and 6 over the rage of speeds 4–12 m/s.

101

Lower speed measurements presumably include acoustic contributions from non- propulsion machinery such as generators and compressors. At low speeds, the acoustic signature of the ship is dominated almost completely by tones from the ship generators [12].

The noise radiated by a ship is modulated at a rate dictated by some parameters of the propeller and engine (number of blades, rotational speed). Evaluation of that modulation provides information on the ship, such as the shaft rotation frequency and number of blades, that can be used for ship classification. The method for estimation of the high frequency envelope modulation is known as DEMON (Detection of Envelope Modulation on Noise) [12-15] and the earlier papers describing this method were published about 50 years ago [13]. The DEMON spectra are the basis for various algorithms of ship classification. The DEMON algorithm utilizes the acoustic signal in a relatively high frequency band (above several kHz) where the ambient noise is much less than in the low frequency band.

The gathered experience regarding acoustic signatures and signal processing led to the developed automated classification algorithms for submarine detection, tracking and classification. Figure 38 shows the Autonomous Passive Acoustic Classification System (APACS) suggested by Lockheed Martin [16]. APACS is designed to automate contact classification by examining the statistical outputs of recorded acoustic signals, thereby reducing the workload on skilled operators and permitting them to spend more time interrogating unknown acoustic contacts. •

Figure 38. APACS system concept; acoustic contacts are automatically classified into five standard categories of unknown, merchant, trawler, light craft and TOI (Target of Interest). • Classification Algorithms - Decision Tree

102

The first attempt of classification at CSR was done for classification of ships in the Hudson River using Decision Tree algorithm for limited set of acoustic signatures .

The classification objective is to identify vessels (ferry, speed boat, sail boat etc) based on a training set of acoustic signature whose group-label is previously known and then be able to embrace any new observation. The general process of classification is placing individual vessels into groups labels based on quantitative information of its attributes. Attributes are: an exhaustive and structured set of well described categories that are usually denoted by a numerical code. In essence the attributes are the preparation for the classification algorithm construction. A wide range of classification algorithms have been studied in various fields, some of which have been applied to acoustics signature classification. Common classification algorithms mentioned in acoustics studies are: Neural Network, K-nearest neighbors, Gaussian and Decision tree. Due to its simplicity and ability to handle a mix type data set, the decision tree model was chosen as the classification algorithm for this case study. The decision tree uses relative entropy or Kullback-Leibler (KL) to study the contrast between two or more probability vectors. This approach sprung from information theory [17,18]. A typical decision tree encodes in a form of tree, where data passes through branch like nodes, constructed from the attributes-rule mentioned earlier and eventually flows through , to the final leafs representing the group label (ferry, sail boat, speed boat etc). The node selection is done by selecting the attribute that divides the inhomogeneous data into minimal inhomogeneous subsets using entropy calculations (Kullback-Leibler method).

We applied DEMON (Detection of Envelope Modulation on Noise) analysis; for ship classification, it is one of the most reliable acoustic means for ship noise detection and classifications.

DEMON analysis construction involves the following algorithm:

1) Measured signal is divided into parts of 1/1000 of a second. In our case, since the signals were measured at 200 kSamples/sec these parts contain 200 measured points.

2) At each part, the measured samples are squared, mean value of the results is calculated, and then square root of the result is computed. Hence out of initial 200 points in each signal part we have received 1 point for further analysis.

3) Some predefined number (e.g. 2048) of these new calculated subsequent points is collected; their mean value is calculated and then subtracted from initial points. The results are analyzed using FFT Power Spectrum.

4) Steps 1-3 are repeated for the rest of the measured signals.

Examples of the recorded DEMON spectra for several boats are presented in Figures 39 and 40. 103

Figure 39. DEMON spectra of various vessels recorded in the Hudson River.

Based on the acoustic signature analysis, the following table provides the attributes and their associated splitting sub-populations that were significant dividers in the classification process:

An example of the attributes extraction of ferry_A and ferry_B is shown in Fig.40 and their attributes values are shown in the Table.11. These observation models allow the classification algorithm to separate and therefore identify the ferries from each other and from all other vessels in the water.

104

Table 11. Attributes description and subpopulation values

Figure 40. Illustration of the selection diagram for two types of ferries.

Table 12. Attributes values for Ferry A and Ferry B

The following decision tree was constructed from the DEMON signatures of 20 recorded boats based on the calculation of the degree of homogeneously of the three group labels ferry_A, ferry_B, and Not ferry.

105

Figure 41. Decision tree used for ferry classification.

This case study has demonstrated the effectiveness of utilizing DEMON acoustic signatures for boat identification. Attributes for classification were extracted from the boat signatures and the simplified decision tree was built. Future work is needed in maintaining a catalog for acoustic signatures; developing a library of few hundred boats signatures will allow for more accurate classification of vessels

Classification approach based on Machine Learning

Student team attended Stevens Summer Research Institute considered opportunities of using Machine Learning based method of vessel classification from passive acoustic signatures. Machine Learning Algorithms (ML’s) are computational data sorting methods that, among other attributes, are capable of making generalizations about a classification. That is to say that ML’s can classify unknown examples based on a data set of known examples. Such systems classify based on closest resemblance rather than exact

106 matching. This makes them particularly well suited for vessel classification in high traffic areas where it may not be possible to get an acoustic sample for every vessel that may pass by the hydrophone.

CSR has previously collected approximately 1000 vessel acoustic signatures from the Hudson River using the Stevens Passive Acoustic Detection System (SPADES). This system consists of four hydrophones used for the acquisition and analysis of acoustic signals. Video cameras, marine radar, and AIS (Automated Identification System) information were recorded at the same time as the recordings from the hydrophones. The use of these sensors is important to relate the underwater acoustic signals or hydroacoustics to its source and in creating a new database with all corresponding information.

The first step of the research is to extract acoustic signatures of different types of vessels by observing the videos and finding a time where only one vessel was present. Using the time of the video marine radar and AIS information (if available) is used to validate the presence of the vessel and extract more information about it from the AIS like: vessel name, size, and type of cargo. AIS information is not always available because the International Maritime Organization's International Convention for the Safety of Life at Sea only requires that international voyaging vessels with gross tonnage (GT) of 300 or more tons, and all passenger vessels regardless of size possess AIS. After validating the presence of the vessel the raw acoustic file is found according to its timestamp to do further analysis.

The raw acoustic files are input into a LabView program called Signal Analyzer created at Stevens Institute, for further analysis. This program can do various types of analysis on the acoustic signals such as: Fourier transform, cross-correlation, and a method of envelope modulation known as DEMON (Detection of Envelope Modulation on Noise). See Figure 42 for an example of Signal Analyzer analysis on a acoustic file that corresponds to a Ferry.

107

Figure 42(a). Picture captured from optical sensors that shows that only a Ferry was passing in front of the hydrophones at 10:42:25 UTC (6:42:25 AM local time) on the date 7/19/2011

Figure 42(b). DEMON Spectra (top graph) and DEMON Spectogram (bottom graph) of the acoustic file of the vessel in figure 42(a)

108 In order to make a semi-automated vessel classification system the DEMON Spectra was used because it can be easily noted that these signals have different characteristics for each type of vessel as shown in Figure 43.

-3 x 10 Demon 3

2.5

1.5 Spectrum

0.5

0 0 5 10 15 20 25 30 35 40 45 50 Frequency (Hz) Figure 43. DEMON spectra of passing boats

The features for classification should be selected such that they have the best discrimination capabilities regarding noise and the similarities between vessel signatures. Due to the nature of the signals as seen in Figure 43 the most important things to observe in them are: the fundamental frequency and the harmonics. The fundamental frequency (FF) is the frequency with the highest intensity and the harmonics are integer multiples or submultiples of this frequency.[19] The feature vector was chosen such as it contains the number of harmonics, the FF, and three ratios of harmonics over the FF (if less than three harmonics present these values are zero). This vector is extracted from each DEMON Spectrum and then exported to a Excel file and an Attribute Related File Format (ARFF) file.

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Over the course of the project several methods were used to develop the test data set and several sets were tried before the test set was finalized. As previously described, the final data set included four distinct vessel categories and 477 total instances. Table 13 shows a break-down of how many instances of each vessel are present in the data set.

Category Total Instances Unique Vessels Ferry 53 3 Circle line 80 2 Speed Boat 291 2 DEP Sludge Boat 53 1 Table 13. Data set break down

Once the data selection was completed and the key features were extracted the database was saved in an Attribute Related File Format or ARFF file. This was then imported into a program called Weka [20]. Weka is an open source program developed at the University of Waikato in New Zealand. Weka incorporates a number of pre-written ML’s into a “Knowledge Flow” environment that allows a user to apply multiple ML’s simultaneously.

Once the ARFF file has been loaded it is fed through a preprocessing step called a cross validation fold generator. This splits the data into a number of sections that will be used to train and test the ML’s. Each section is used in turn to train an ML which is then tested using the remaining sections. The program then selects the training and testing split that produced the greatest accuracy in the ML. This cross fold validation method takes many more steps than generating separate testing and training sets and is therefore limited in its usefulness for very large data sets. When there is a limited amount of data available cross folding becomes extremely valuable as a way to arrive at a well trained ML.

After the cross validation process is complete for each of the ML’s being tested the results are displayed in a simple text readout. The most accessible metrics of ML performance are the percentages of correctly and incorrectly classified instances, were the main basis for determining the success or failure of a particular ML. It is also important to examine how well a ML performed on each of the classes it was meant to separate. This was done by examining the false and true positive rates for each class, and by consulting the confusion matrix for each ML.

Ultimately three types of classifier were considered in this study; a multi layered perceptron, a random tree and a random forest,. The performance of each classifier is displayed in Table 14.

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ML: % Properly Classified Kappa Statistic

Multi-Layered Perceptron 82.4% 0.6596

Random Tree 93.5% 0.8889

Random Forest 95.8% 0.9278 Table 14. Performance of various types of classifier.

Table 15 shows a breakdown of the rate of true positives for each vessel class. This represents the instances of each vessel that were properly classified by each ML.

Multi-Layered Perceptron Random Tree Random Forest

Ferry 0.849 0.925 1.0

Circleline 0.925 0.975 1.0

Speed Boat 0.931 0.938 0.962 Table 15. Table showing the True Positive Rate of each ML for each class of vessel

References

1. R.J.Urick. Principles of Underwater sound. McGraw-Hill Book Comp., 1983 2. Physics of Sound in the Sea, Summary Technical Report of Division 6, National Defense Research Committee, reprinted by Dept. of the Navy, Naval Material Command , 1969. 3. Bublić, I., Skelin, D., Vukadin, P. Measurement of vessel underwater noise signature Proceedings Elmar - International Symposium Electronics in Marine 2, art. no. 4747529, pp. 407-410. 2008. 4. Vaicaitis, Rimas, Mixson, John S. 1 REVIEW OF RESEARCH ON STRUCTUREBORNE NOISE Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference (pt 2), pp. 587-601, 1985. 5. Mer, L., Esparcieux, P., Fournier, P. 2006 International Conference - Warship 2006: Future Surface Warships , pp. 95-101. 6. M Trevorrow, B Vasiliev, S Vagle, Directionality and maneuvering effects on a surface ship underwater acoustic signature. J. Acoust. Soc. Am. 124 2, August 2008. 7. P. Arveson and D. Vendittis, “Radiated noise characteristics of a modern cargo ship,” J. Acoust. Soc. Am. 107, 118–129 2000.

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8. L. M. Gray and D. S. Greeley, ‘‘Source level model for propeller blade rate radiation for the world’s merchant fleet,’’ J. Acoust. Soc. Am. 67, 516–522 ~1980. 9. M. Strasberg, ‘‘Propeller Cavitation Noise After 35 Years of Study,’’ in Proceedings Symposium on Noise and Fluid Engineering ~ASME, New York, 1977, pp. 89–99. 10. W. K. Blake, ‘‘Aero-Hydroacoustics for Ships,’’ David Taylor Naval Ship Research & Development Center Report 84/010, June 1984, Chap. 4. 11. D. Ross, Mechanics of Underwater Noise Peninsula, Los Altos, CA, 1987. 12. J. G. Lourens, “Classification of ships using underwater radiated noise,” Communications and Signal Processing, COMSIG 88, 13–18 1988. 13. . F. B. Tuteur, “Detection of wide-band signals modulated by a low-frequency sinusoid”,Appendix A-4 from Processing of Data from Sonar Systems, by R. A. MacDonald et al.,Yale University (1963). 14. A. A. Kudryavtsev, K. P. Luginets, and A. I. Mashoshin, “Amplitude modulation of underwater noise produced by seagoing vessels”, Acoustical Physics 49 (2003). 15. Bao, X. Wang, Z. Tao, Q. Wang, and S. Du, “Adaptive extraction of modulation for cavitation noise”, The Journal of the Acoustical Society of America 126, 3106–3113 (2009). 16. Autonomous Passive Acoustic Classification System (APACS) . CEROS Project Description. Lockheed Martin , 2005. http://www.ceros.org/documents/projectdescriptions/16%20PD_LMOD_APACS _51949.pdf 17. S. Kullback, Information Theory and Statistics (New York: Dover Publications, 1959. 18. S. Kullback and R.A. Leibler, “On Information and Sufficiency,” Annals of Mathematical Statistics 22, no. 79. 1986. 19. Ahmad Aljaafreh ,Liang Dong. “An Evaluation of Feature Extraction Methods for VehicleClassiﬁcation Based On Acoustic Signals.” (2010). [Online]. Available: http://homepages.wmich.edu/~ldong/paper/0106.pdf [June 20,2012]. 20. Marsland, Stephen. Machine Learning: An Algorithmic Perspective. Boca Raton: CRC, 2009. Print

Outcomes

1. Leveraged Resources

• The effort described here employs the facilities of the Maritime Security Lab. The passive acoustic system was initially supported by a grant from the Office of Naval Research. The SPADES 2 development and field tests with small boats are partially supported by DHS S&T Borders and Maritime sponsorship.

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• Improvement of MSL abilities and installation of two radars, AIS and new IR/Optic surveillance system was done with support and in collaboration with 4D Security.

2. Publications

The first publication was student-led and was published in a key Homeland Security Journal.

T. Meir, M. Tsionsky, A. Sutin, H. Salloum. Decision Learning Algorithm for Acoustic Vessel Classification. Homeland Security Affairs, DHS Centers of Excellence Science and Technology Student Papers (March 2012) http://www.hsaj.org/?article=supplement4.3

A. Sutin, M. Bruno, H. Salloum, J. Dzielski, N. Sedunov, A. Sedunov, M. Tsionsky, L. Fillinger, A.J. Hunter, M. Zampolli, M.C. Clarijs. Passive acoustic diver detection in a Dutch harbor. WaterSide Security 2012 conference, Singapore, 2012.

A. Sutin, M. Bruno, H. Salloum, B. Bunin, M. Tsionsky, N. Sedunov, A. Sedunov Acoustic detection, tracking, and classification of boats in the Hudson river. WaterSide Security 2012 conference, Singapore, 2012.

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Task 1.4, Decision-Support Systems (Note: there are two PIs on Task 1.4, J. Nickerson, Stevens Institute of Technology, and B. Reagor, Monmouth University)

Task 1.4 Project 1. Stevens Institute of Technology (Dr. Jeff Nickerson, PI) TRL 4 Coordinating interests in emergency decision-making

Project Description and Significance to Stakeholders

The work undertaken in the past year aimed to explore inter-agency and interpersonal coordination to improve decision-making. Security operations, and especially responses to emergencies, require collaborations between multiple agencies. The stakeholders for this project continue to be federal, state, and local agencies that collaborate in security and emergency response, and include the following agencies and departments: a. U.S. Coast Guard b. Federal Emergency Management Agency c. Transportation Security Administration d. State port authorities e. Local police and fire departments

The Department of Homeland Security is responsible for promoting and facilitating such collaborations among its subsidiary agencies and between federal, state, and local authorities. Successful collaboration often hinges on the coordination of both divergent instrumental interests—resources and rewards— and divergent interpersonal interests— e.g. identity, status, and personal values. Over the last year, we investigated ways to improve inter-agency collaboration by promoting the coordination of instrumental and interpersonal interests. Throughout our ongoing research, we have demonstrated that perspective taking helps collaborators look past conflict towards common interests. Simply making an effort to imagine another perspective helps collaborators override differences of resources and identity. They can, then, envision sharing a “bigger pie” and make decisions that benefit a wider range of stakeholders. These decisions represent a form of “social rationality.” Does this social rationality promote rationality more generally?

In year four, we continued to investigate the ways in which perspective taking mitigates the impact of instrumental and interpersonal conflicts on the efficacy of collaboration and collaborative decision-making. The key question explored during year four’s work was whether perspective taking also mitigates the impact of cognitive biases in many of the core functions of DHS. To investigate this research question, we designed several Web- based “games” based on emergency scenarios as the basis of three main sets of experiments, covering a total of 7 conditions related to common cognitive biases which are given in table one. These were “games” both in the sense used by game theorists— that is, scenarios that require strategic decision-making—and in the everyday sense of recreational contests or challenges. In some games, the “players” will bargain over an instrumental good. In other games, the players will bargain over an interpersonal good. 114

Oftentimes, players will bargain over both types of goods. Across games, players must apply these goods towards responding to some risk or threat.

Bargaining often unfolds over roughly two stages: the tacit bargaining stage, when negotiators independently decide how much to demand and how little to accept, and the explicit bargaining stage, when they negotiate face-to-face. The earlier independent decisions ultimately exert strong influence on the later negotiated agreements. We use tacit bargaining games (games without communication and limited knowledge of one’s partner) to investigate how might people mitigate anticipated conflicts before face-to-face bargaining. We use explicit bargaining games (games with communication and increasing knowledge of one’s partner) to investigate how players resolve conflicting interests during a collaborative task. As in Year 3, we were interested in when and why players coordinate their interests. In Year 4, we were especially interested in when and why the process of coordination dampens cognitive biases and promotes decisions that yield a greater good.

As stated earlier, DHS is responsible for promoting and facilitating collaborations that exert the full force and utility of federal, state, and local resources. Improved coordination, e.g. inter-agency resource sharing, has also become more critical due to increasing budgetary constraints across various agencies. This project can offer empirical clues as to why inter-agency collaborations can sometimes devolve into multiple parallel efforts, lacking collaborative force and utility. Further, this project can offer metrics and protocols for more productive inter-agency negotiation and collaboration. Thus, the benefits to DHS are two-fold: awareness of various psychological effects on collaborative efforts, and ways to dampen or, even, harness those effects.

Milestones Met

There were three main milestones defined for year four’s work. The first milestone was to “successfully develop and demonstrate Web-base “games” based on emergency scenarios.” The second milestone was to “successfully demonstrate the application of the Web-based “games” in the study of coordination via various types of bargaining.” Milestones one and two were completed, using Web-based “games” that were successfully developed for deployment on Amazon’s mechanical turk. Using Amazon’s mechanical turk as the platform for the experimental games enabled the collection of data from participants of various demographics. The developed “games” were successfully tested and deployed specifically for the experimental topics listed in Table 1. Table one shows the relevant experiments categorized by topic and designed to shed insight into coordinating interests in emergency decision-making.

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Experiment Topics Status

Tacit Coordination of Resource Conflicts

The Effects of Identity on Resource Conflicts completed

Social-Cognition Dampens Identity Conflicts completed

The Effects of Power Asymmetries on Resource completed Conflicts

Social-Cognition Dampens Power Conflicts completed

Tacit Coordination in Emergency Response

Dampening Resource Conflicts completed

Explicit Coordination in Emergency Response

Resource Coordination completed

Coordinating Cultural Identity completed Table 6: Summary of the milestones met in the last year categorized according to the experiment topics.

The third milestone was to “Develop protocols for use by the first responder community in facilitating more effective inter-agency negotiation and collaboration.” Summaries of the findings for the studies performed on the experimental topics given in table one are contained in the two following manuscripts, “Tort Reform: Cognitive Perspective Taking Promotes Attributions of “Oblique” Intent for Side-Effects of Intentional Action,” and “Revisiting ‘The [Social-Cognitive] Nature Of Salience’: Social-Cognitive Effort Focuses Attention on the "Next-Most Obvious" Bargaining Compromise-Equal Shares.” Based on these findings the key recommendation during the development of protocols for use by the first responder community in promoting inter-agency negotiation, is to integrate mechanisms that encourage perspective-taking.

The first author of the two manuscripts, John Voiklis, finished his engagement with Stevens Institute of Technology, but is continuing to refine the manuscripts for publication.

Outcomes

1. Publications

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These working papers are in the process of being turned into manuscripts for publications.

Voiklis, J., and Nickerson, J. V., Tort Reform: Cognitive Perspective Taking Promotes Attributions of “Oblique” Intent for Side- Effects of Intentional Action, Available at http://ssrn.com/abstract=2144205

Voiklis, J., and Nickerson, J. V., Revisiting “The [Social-Cognitive] Nature Of Salience”: Social-Cognitive Effort Focuses Attention on the "Next-Most Obvious" Bargaining Compromise--Equal Shares. Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2136587

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Task 1.4, Project 2. Monmouth University (Dr. Barbara T. Reagor, PI.) TRL 6

Project Objectives and Significance to Stakeholders

The Rapid Response Institute (RRI) lead by Dr. Barbara T Reagor, Director, was established in 2004 to leverage the University’s extant software engineering, modeling, and simulation talent and research capabilities to support military and civilian rapid decision making to Prevent, Protect, Respond and Recover in the event of a homeland security, homeland defense or all hazard disaster events.

The Urban Coast Institute (UCI) lead by Anthony Macdonald Esq., Director, was established in 2005 to facilitate the application of the available best science and research to address coastal and ocean policy issues and to leverage these applications in the development of Coastal Resilient Communities.

Together UCI and RRI support CSR relevant initiatives including the Summer Research Institute, workshops, outreach, and training. During Year 4 we have contributed to the CSR and its stakeholders in several areas.

Milestones Met

MU Faculty Participated in CSR Summer Research Institute in July 2011 to Present Emergency Strategies, Policies and Educational Outreach in Support of 2011 SRP Ports and Harbor Security Research;

• Provided educational presentations and exercises for SRI Students on Emergency Management, Piracy Impacts to the Maritime Environment, Maritime Piracy and Policy Issues, Piracy and Terrorism the new connection, and Managing Military and Civilian Messaging

• Previous MU High School Summer Researcher, Greg Sciarretta, Binghamton University, NY participated in the 2011 CSR Summer Research Institute and assisted in the development of the Magello Web Portal.

RRI continued the 2011 STEM Research program for High School Students to assist in the development of hazard resilient coastal community indicators and to create a framework to enhance stewardship, storm readiness and vulnerability reduction;

• In August of 2011 our web portal for Emergency Preparedness, Emergency Management and Flood Prediction utilizing real-time sensor data (Floodview) was used to support 13 local communities for evacuation warnings as a result of Hurricane Irene.

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Actual water Hurricane Irene level height Evacuation change due to Ordered hurricane passing

Power Outage

• With Year 4 funds, additional sensors have been added to the system. It currently provides flood weather and flood height data to 13 municipalities, and includes NOAA water height sensors. A tool was prototyped to enable easy addition of any available sensor data and its visualization.

119 New Sensors added from NOAA

Original Floodview Existing Sensors from Year 3

120 • After discussions with CSR and refinement of Year 4 Deliverables, students created a “Man Overboard” program using NYHOPS Surface Current Vector Data that takes previously stored vector data and projects the position of the “Man Overboard” surface location. Man Overboard

''1

3 2 Obtained 4 sequential sets of surface vector data from NYHOPS for locations 1, 2 and 3 ''

121 Location 1 data – 4 sequential measurements

Location 2 data – Shows different directions in same area

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Location 3 data shows changes in direction with small time integrals.

• As a result with CSR discussion, students have created a KML button for Magello with the newly added sensors of Floodview and are prepared to create a permanent button for Magello when access and funding are available to do the hard coding require.

• As a result of discussions with CSR and UCI a new prototype utility SNAFU was also developed during this period. This meets the need of general allowed users of the Floodview system to be notified if any change of water depth of any sensor (USCG, NOAA, Floodview) with parameters such as sensor, failure, High or Low minimums exceeded or prediction based of comparison of multiple organization sensors that could impact the coastal community. SNAFU sends text and alerts to all registered individuals for Floodview and does not require individual registration to the original data collection agencies (ie. Stevens, NOAA or USGS).

MU’s RRI & UCI have participated in all of the Year – 4 activities of CSR. Our participation for the year included:

• Presentation at the Mid -Year Conference: February 8, 2012 At Stevens • RRI Presentations and outside speaker coordination for the CSR SRI • Participated in meetings at Stevens and conference calls as needed • Previous MU High School Summer Researcher Timothy Higgins (Drexel) participated in the 2012 CSR Summer Research Program for additional refinement to Magello. • Ms. Lauren Landrigan has continued her PhD Research program at Stevens Institute (Current GPA 4.0) working for Dr. Jon Wade School in support of RRI – Rises and Stevens – CSR • Supported CSR through our collaborations and current programs

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2. MTS Resiliency

Task 2.1 Resiliency Strategies for Port Systems Stevens Institute of Technology (B Sauser and J. Emmanuel Ramirez-Marquez, PIs)

Project Objectives and Significance to Stakeholders

The Year 4 effort described here seeks to further increase our understanding of the various potential failure scenarios, and through the use of our data, metrics and models develop an understanding of the tradeoff among different protection and restoration policies that improve the ability of a port to withstand or “bounce back” rapidly from disruptions.

Milestones Met

The specific milestones envisioned for the project were scaled back in response to budget constraints. The research conducted focused on specific plans for port resiliency.

The research centered on developing a framework for the assessment of resiliency benefits in terms of economics for marine terminals. This framework aims to assist supply network stakeholders to create investment strategies for the allocation of available finances for supply chain security, and to retain business continuity during or immediately following disruptions. Researchers mapped a methodology for creating port resiliency plans that could apply across a wide array of ports. The process was applied to a particular port in West Virginia and led to additional work funded external to DHS.

Outcomes

1. Publications H. Gajjar, Development of economic resiliency assessment framework for marine terminals, at the Society of American Military Engineers' (SAME) Critical Infrastructure Symposium on April 24, 2012, in Arlington,VA.

Abstract: In today’s world, seaports have become critical nodes in the supply network. Marine terminals provide the interface and connection between vessels and landside transport modes where performance is measured as “throughput”. Disruption in maritime and landside logistic services can cause significant economic impacts. Ports contain many critical infrastructures and they are considered extremely vital for nation’s economic growth. It is not only important to protect these critical infrastructures but also make them resilient so that it can recover faster following a disruptive event. This paper talks about developing an economic resiliency assessment framework which will help stakeholders to create investment strategies

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for the allocation of the available finance for port security and to retain business continuity during or following any disruptions and hence serves as a resilient logistics system.

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Task 2.3 Port Resilience Project (Note: there are two PIs on Task 2.3, M. Mattingley, The Mattingley Group; and J. Rice, MIT) TRL 6

Task 2.3 , Project 1. The Mattingley Group, LLC (M. Mattingley, PI)

Project Objectives and Significance to Stakeholders

MG Group seeks to leverage its expertise in port and supply chain security to improve the ability of US ports to provide and maintain an acceptable level of service in the face of major changes or disruptions. A key objective is the development of methodologies that ports can use to evaluate their resilience and strategies for port authorities to improve their resilience. This will include the identification and prioritization of issues and failure nodes by port authorities and stakeholders in coordination with the CSR Team. In addition to relying on sensors and technologies, resiliency can be enhanced through increased adaptive capacity and building redundancy into the design and operation of ports. To achieve this objective, ports must be seen as a network of physical nodes with multiple layers that include freight processing, customs and immigration, and freight intermodal connection systems. Such a systemic view provides a large degree of flexibility and combination of response alternatives, adding to the resilience of port operations design and management. MG Group will assist in the preparation and development of such policies through direct and indirect interaction with port and supply chain stakeholders.

Milestones Met

1. Develop a proposed set of actions for creating port resilience; Continued analysis for lessons learned of real-world port and supply chain disruptions. This effort built upon prior year reviews. Particular emphasis was devoted to the impact of the Occupy Movement on California Port Operations. Other notable resiliency concerns included the impact of Post Panamax vessels on US coastal ports and modernization of inland waterways; impact of Port Fourchon LA infrastructure improvements on petroleum imports comprising nearly 20% of the US total oil supply; the disruption in inland waterway shipping due to low water level caused by drought conditions; Columbia River Bar and Houston Ship Channel closures; AIS Vessel Tracking and Data Analysis; and the Administration’s new National Strategy for Global Supply Chain Security. Focus included effectiveness of preparation, response, and recovery plans; impact of disruptions on port capacity; rail and road infrastructure; ship diversion to alternate ports; public safety issues; restoration of police, fire, and medical 126

capabilities; evacuation and temporary housing concerns; debris removal/cleanup; business loss and unemployment; economic recovery; and mitigation measures to address these issues.

Assess status and refine scope of failure mode analysis to identify supply chain vulnerabilities to disruptions, impact on port operations, communications, infrastructure, personnel.

2. Update, refine, and publish the US port capacity study which will include a vulnerability assessment: Contributed to update of port capacity study to determine traffic volumes for selected ports and ability of alternate ports to absorb additional traffic in event of a major port disruption with focus on operating hours, labor issues, intermodal capability, cargo types and specialized handling requirements, vessel configuration and dockside crane capabilities. Recommended expansion of scope of port capacity review to include inland ports and waterways.

3. Provide critical data and structural input to the modeling effort: Coordinate application of these studies into beta version of MIT Port Mapper tool; evaluated Port Mapper trial version.

4. Perform additional port survey(s) and complete and publish the analysis: Planned, coordinated, and conducted field site visits to the Tulsa Port of Catoosa, Port 33 Johnston Terminal; Arkansas River Lock & Dam #18; USCG Marine Safety Detachment Fort Smith AR; University of Arkansas Mack-Blackwell Rural Transportation Center; and Port of Baltimore, CSX Terminal to benefit from lessons learned in real-world port operations, review port disruption historical events and impact on critical systems, review stakeholder agreements for consultation and cooperation, port recovery plans, decision-making processes and authority. This builds upon previous field visits to the ports of Los Angeles, Long Beach, Hueneme CA, Houston, Boston, New York/New Jersey, Seattle and Tacoma. These visits provided insight into the current perceptions of the maritime community regarding resilience, the impact of state, federal, and local laws and regulations on port operations, causes of port disruptions, resolution of such disruptions, and the average length of such disruptions.

Expanded focus of study to add inland ports and waterways in addition to coastal ports. This included initial visits to Tulsa Port of Catoosa, Port 33 Johnston Terminal; Arkansas River Lock & Dam #18; USCG Marine Safety Detachment Fort Smith AR; University of Arkansas Mack-Blackwell Rural Transportation Center. 127

Particular emphasis was given to the aging infrastructure of the lock and dam systems; options for repair and/or replacement; economic impact of disruption in navigable waterways. Data gathered during these visits is being evaluated against the previously gathered coastal port data to validate the issues common to both as well as those unique to inland ports.

Outcomes

1. Resources Leveraged

a. Port Authority of New York & New Jersey i. Provided first hand experience on development of port recovery plans ii. Provided real world expertise on port disruption scenarios iii. Provided lessons learned on first response capabilities, stakeholder coordination and involvement, dealing with multiple political jurisdictions, and business continuity

b. Massachusetts Institute of Technology Center for Transportation and Logistics i. Provides expertise in supply chain efficiency and security studies ii. Provides extensive background in data analysis and modeling and simulation capabilities iii. Capability to bring research resources to bear on resiliency issues and identify mitigation strategies

c. Tulsa Port of Catoosa i. Identification of port operations, processes, and systems vulnerable to disruption. ii. Enhance understanding of the dynamics of port operations as they relate to resiliency.

d. Port 33 Johnston Terminal i. Identification of port operations, processes, and systems vulnerable to disruption. ii. Enhance understanding of the dynamics of port operations as they relate to resiliency.

e. Port of Baltimore, CSX Terminal i. Identification of port operations, processes, and systems vulnerable to disruption. ii. Enhance understanding of the dynamics of port operations as they relate to resiliency. 128

f. University of Minnesota - National Center for Food Protection and Defense i. Draw upon experience in reducing supply chain vulnerabilities ii. Identify similarities between strengthening resiliency in maritime supply chains and food supply chains iii. Compare mitigation efforts to minimize impact of disruptions through effective response and recovery measures g. University of Arkansas – Mack-Blackwell Rural Transportation Center i. Provides expertise on security of the intermodal transportation systems of the United States at the local, state, and national levels. ii. Develops solutions for solving critical scientific and technological issues related to transportation security iii. Evaluates research and develops technologies to protect US transportation networks

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Task 2.3.2 Port and Supply Chain Resiliency Massachusetts Institute of Technology (Mr. James B. Rice, Jr., PI)

Project Objectives and Significance to Stakeholders

The project objectives for Port and Supply Chain Resiliency research continue as follows: • Identify critical processes and systems of the MTS that need to be resilient • Identify methods for making critical MTS processes and systems resilient.

In Year 4, the research focused on integrating and leveraging the respective and previous CSR resilience contributions to create useful tools and methods for practitioners and planners. Additionally, the team will address inland waterways and environments and their need for resilience. The priority effort was therefore focused:

US Port Capacity Analysis Tool and Application Development: The Year 1-3 study of US port capacity provided the best opportunity to quickly leverage work-to-date into practical solutions. In particular, the US port capacity study served as the foundation for the development of a US port capacity assessment and reallocation tool. This work will enable additional analysis and vulnerability assessments.

Secondary objectives included: • Continued development of actions for creating port resilience. • Port Survey(s). Complete and publish the analysis from the US Port Survey, consider developing a follow-up survey of stakeholders. • Provide critical data and structural input to the modeling effort. Data from the port survey, field visits, interviews, and failure mode analysis will be shared as needed to inform a modeling effort by the broader CSR port resilience team.

Our work entails identifying critical processes and systems, and identifying methods to make systems resilient. The work involved consolidation of observations from various elements of the CSR Port Resilience Team work to date. This included distilling observations from the survey, field visits and analyses from the port capacity analysis tool. Some of the observations have been published ("Failure modes in the maritime transportation system – a functional approach to throughput vulnerability" Maritime Policy and Management, Volume 38, Issue 6, 2011, by BERLE, Ø., RICE JR, J.B., and ASBJØRNSLETT, B.E.) and also included in various project white papers (http://ctl.mit.edu/research/port_resilience_project_references_and_publicat ions). A final synthesis is in process.

Milestones Met

1. US Port Capacity Analysis Tool and Application Development:

Progress In year 2 and 3, the MIT Port Resilience Team initiated a study assessing the ability of the 310+ ports in the US to handle disruptions. The team created a spreadsheet-based tool utilizing publicly available data (primarily from the Army Corps of Engineers (ACOE) database of port volumes). The tool enables users to assess the ability of the domestic US ports to absorb cargo that may need to be reallocated to different domestic ports from disrupted ports. This analysis can be conducted at the level of Standard Industry Classification (SIC) code. Among other capabilities, the tool can: - identify the distance and the average number of stops required to relocate volume from a disrupted port (this only applied to the volume in port at the time of disruption); - identify the maximum current capacity utilization in order for the remaining ports to absorb the volume from any one port closure. For each commodity class, a calculation can be made of the maximum level of capacity utilization of all other ports serving that commodity class in order for the remaining ports to absorb the displaced volume; - identify options for reallocating SIC code-level cargo to various ports across the system of ports in the domestic US; - develop scenarios for various disruption situations and assess options for cargo reallocation; and - serve as the foundation for more robust disruption scenario development for policy makers and planners.

During Year 4, the capabilities of the tool were tested and analyzed. Most importantly, the tool served as the design basis and analytical foundation for the development of a web-based application called Port Mapper. Funding for this work came from CSR and was supplemented by approximately $50,000 by the Integrated Supply Chain Management Program at MIT. These developments were reviewed on several different occasions with various representatives of several US government agencies (DHS, USCG, MARAD, NMIO). The specifics of the tool and web-based application are described immediately below.

Port Mapper Overview As noted above, the MIT Port Resilience team has been working on understanding the capacity constraints that affect the potential resilience of the Maritime Transportation System in the US during Years 1-3. Via this work, the team developed a spreadsheet- based tool that addresses some of the key questions: - Where could cargo move to if there were a disruption at a major US port? - What other ports handle the same cargo types as the disrupted port? - How far away are those ports? - Does the US system of ports have enough capacity to handle a disruption to a major US port? - How much additional port capacity is necessary for the US ports to handle a disruption and avoid significant delays and costs to the US economy? 131

During Year 4, the team has taken that spreadsheet-based tool and used it as the basis of a web-based application (MIT Port Mapper, a working prototype). The Port Mapper integrates some of the cargo allocation analytics from the spreadsheet-based tool with a Google earth mapping tool, and allows a user to identify potential destinations for cargo based on cargo SIC classification, location or size. This makes the analysis available to a broader set of users, including policy makers who may wish to use the application to conduct scenario analysis considering disruptions at any of the 310+ ports in the continental US. The Port Mapper does not currently have all the analytical functionality as the spreadsheet-based tool, as this was developed as a beta version to demonstrate the potential and serve as a basis for soliciting input on future development needs and interests. The application can be accessed by visiting: http://portmap.mit.edu/ApplicationOverview.htm

The MIT Port Resilience Team provided briefings and detailed reviews of their recent work, focusing on the development of the capacity analysis tool and it's web-based beta-grade application called Port Mapper. Overall reception was positive with all audiences, stimulated suggestions for next versions and improvements.

October 6, 2011, Port of Catoosa, Catoosa OK; Presentation to Port Community

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November 9, 2011, Rutgers University, NJ; Maritime Risk Symposium

February 8, 2012, Stevens Institute of Technology, Hoboken NJ; MIREES Review Meeting

March 15, 2012, USCG Headquarters, Washington, DC; Briefing to USCG, DHS Community organized by USCG

April 11, 2012; Virtual presentation at monthly briefing to the National Interagency Group, organized by the National Maritime Intelligence-Integration Office

May 2, 2012, MIT, Cambridge Ma.; Meeting at MIT with Rear Admiral R. V. Hoppa of the National Maritime Intelligence-Integration Office

Port Mapper Current Capabilities

At present, the Port Mapper application is set up to plot and link ports based on the commodities that they handle. The user makes two selections: - Choice of state or choice of SIC Group/SIC Family or SIC Description - Choice of all ports or top ten ports, or a specific port by name (note that once you select a specific SIC type, only those ports that handle that SIC will be displayed)

Once the selections are made, the system displays results, plotting and linking the ports handling the SIC from either the specific port selected or the largest port handling that commodity if All Ports or Top 10 Ports was selected.

A few examples may help illustrate the possibilities. If one were interested in knowing which ports in the US handle Radioactive Materials, the user would select Chemicals - 3281 Radioactive Material and ALL, which would display all ports handling that particular commodity, displaying the shortest distance path from the largest port to every other port in the continental US that also handles that commodity. If a user were interested in knowing the Top 10 ports that could handle containers if the Port of Savannah were closed, the user would select SIC Group called Container and Top 10, and identify Port of Savannah as Port to Fail. In response, the system would display the top 10 port options for containers with shortest-distance path shown from the Port of Savannah. The user has several options for viewing the various potential ports for reallocation. ii. Other Year 4 Project Objectives

The team made progress against the secondary project objectives in Year 4 as noted and will continue work on these in Year 5:

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• Continued development of actions for creating port resilience. This is an ongoing effort to capture insights as the research progresses. • Port Survey(s). The analysis was completed for the US Port Survey and a report was prepared incorporating the most recent analytical observations from Structural Equation Modeling in an appendix. The team considered conducting a follow up survey but decided to focus efforts on the application development. A follow up survey may be considered in Year 5. • The team consulted with the broader CSR resilience team on model development but was not tasked to provide additional data and input.

Outcomes

1. Resources Leveraged • Port Authority of Tulsa at Catoosa • USCG Marine Safety Detachment Fort Smith AR

2. Publications

Through Year 3, the following preliminary draft reports and white papers were in process or were completed and posted online at: http://ctl.mit.edu/research/port_resilience_project_references_and_publications: a. Working Paper on Port Resilience 2010, by James B. Rice, Jr. and Kai Trepte (in process) b. Failure Mode Analysis – Redux, 2010, by James B. Rice, Jr., MIT CTL Working Paper c. Failure modes in the maritime transportation system – a functional approach to throughput vulnerability, 2010 by Berle, Ø., Rice, J. and Asbjørnslett d. White Paper - Resilience in the U.S. Port Network, by Adrien Gasparini, Visiting Student at MIT CTL from Ecole des Mines, January 2010 e. Revisting Port Capacity: A practical method for Investment and Policy decisions; by Ioannis Lagoudis and James B. Rice, Jr. f. http://ctl.mit.edu/sites/default/files/Lagoudis Rice - Revisiting Port Capacity Paper.pdf"From Manufacturer to Forward Operating Base" by Col. John C. Waller, Visiting Scholar at MIT, in Army Sustainment - Spectrum Section, July- August 2011, pp 50-55 g. DRAFT White Paper - The Impact of Port Disruptions on Water and Land Travel Distances, by Kai Trepte, Research Associate, MIT CTL, Summer 2010 h. DRAFT White Paper - An Exploration of Port Growth in the United States, by Kai Trepte, Research Associate MIT CTL, Summer 2010 i. DRAFT White Paper - Port Investment and Resilience, by Kai Trepte, Research Associate, MIT CTL, Fall 2009

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In Year 4, the following papers, draft reports and white papers were in process or were completed: a. The MIT Port Survey Report, internal draft; b. Introducing Port Resilience and Port Capacity Study in US Ports, by James B. Rice, Jr. and Kai Trepte, paper submitted at July 2011 Supply Chain Security Conference at Imperial College in London; c. Supply Chain Risk Management: Failure Mode Focus for Resilience, by James B. Rice, Jr., paper submitted at July 2011 Supply Chain Security Conference at Imperial College in London; d. An Initial Exploration of Port Capacity Bottlenecks in the United States Port System and The Implications on Resilience by Kai Trepte and James B. Rice, Jr., paper submitted at July 2011 Supply Chain Security Conference at Imperial College in London.

3. Education During the summer of 2012, James Rice worked with one of CSR Summer Institute research teams in application of the principles of the Port Mapper to a port disruption scenario centered around the Port of New Orleans.

Additionally, Rice conducted a supply chain disruption simulation with the CSR Summer Institute Students and presented a review of supply chain resilience for reference.

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IV. EDUCATIONAL ACTIVITIES

1. Educational Programs and Activities

1.1. Professional Development Programs

1.2. Maritime Security Graduate Certificate

1.3. Maritime Systems Master’s Degree Fellowships DHS CDG Awards 1.3.1. 2010 DHS CDG Maritime Systems Fellowship Recipients 1.3.2. 2011 DHS CDG Maritime Systems Fellowship Recipients

1.4. Summer Research Institute 2012 Program Overview 1.4.1. SRI 2012 Program Theme 1.4.2. SRI Guest Speakers 1.4.3. Field Visits and Meetings with Practitioners 1.4.4. Student Participation Criteria 1.4.5. Student Funding and Support 1.4.6. Program Administration 1.4.7. Program Format and Curriculum 1.4.8. Student Teams and Research Projects 1.4.8A. Acoustic Sensor Technologies Team 1.4.8B. Magello Emergency Response Team 1.4.8C. Port Mapper Tool Team 1.4.8D. HF Radar Team

1.5. SRI Assessment 1.5.1. Student Survey Results 1.5.2. SRI 2012 Outcomes and Lessons Learned 1.5.3. Recommendations 1.5.4. Conclusion

1.6. SRI Alumni in the Workforce

1.7. Students Mentored and Supported by CSR Academic Partners

1.8. US Coast Guard Auxiliary Program

1.9. Future Educational Opportunities and Plans

2.0. Education Milestones

1. Educational Programs and Activities CSR is fully committed to enhancing the knowledge, technical skills, and leadership capabilities of our nation’s current and prospective maritime security workforce. Central to the center’s mission, is the transfer of its research and expertise into highly relevant, innovative maritime security-centric educational programs.

Since the center’s inception in 2008, CSR in collaboration with its academic partners, Stevens Institute of Technology, Rutgers University, University of Miami, University of Puerto Rico-Mayaguez, Massachusetts Institute of Technology, and Monmouth University, have worked to develop a comprehensive portfolio of maritime systems and maritime security related educational programs. The center’s corner stone programs include:

• The Summer Research Institute (SRI) • Professional development programs tailored to maritime security practitioners, and • The Maritime Systems Master’s Degree Fellowship program

In Year 4 CSR emphasized its relationships with stakeholders to provide professional development and multi-disciplinary learning opportunities and internships for its students. Representatives from Customs and Border Protection (CBP), the Naval Undersea Warfare Center (NUWC), the Naval Surface Warfare Center (NSWC) and the US Coast Guard (USCG) played key roles in the experiential learning and career development of CSR’s students.

During Year 4 the center continued to leverage its academic partnerships and existing educational programs to support the enrollment of high-potential students in the center’s educational programs and Summer Research Institute.

This section of the CSR Year 4 annual report highlights the Center’s educational activities and notable achievements for 2011.

1.1 Professional Development Programs

During Year 4, CSR co-hosted a Maritime Systems Seminar Series with Stevens Institute of Technology, featuring faculty and invited guest lectures. The seminar series is intended to engage a broad audience of faculty, students, industry and government stakeholders, and the general public in relevant and timely topics related to the Maritime Domain and the Marine Transportation System (MTS). The Maritime Systems Seminar Series is delivered on-campus at Stevens Institute and online via Stevens web platform, Wimba. The 2011 – 2012 seminars are outlined in Table 1 below.

Table 1. Maritime Systems Seminar Series 2011 – 2012. Seminar Faculty/Guest Lecturer Date of Seminar Mississippi and Marrimack Dr. Robert Hetland, August 17, 2012 Contrasting a Big and Small Texas A&M University River. Resiliency of our Physical and Dr. Mitchell Erickson, February 29, 2012 Social Infrastructure. Department of Homeland Security Hawaii Undersea Military Dr. Roy Wilkens, Center July 22, 2011 Munitions Assessment for Island, Maritime and Extreme Environment Security (CIMES)

Previous seminars have included: • Piracy, Terrorism and Technology. Faculty lecturer: Dr. Barry Bunin, Stevens Institute • Maritime Structures in a Changing Environment. Faculty lecturer: Dr. Jon Miller, Stevens Institute • Challenges in the International Maritime Transportation System. Faculty lecturer: Dr. Thomas Wakeman, Stevens Institute • The Marine Transportation System: A Systems Engineering Opportunity? Faculty lecturer: Dr. Michael Pennotti, Stevens Institute In addition to co-sponsoring the Maritime Systems Seminar Series, CSR arranged to deliver its Port Security and Sensing Technologies professional development course at the National Maritime Training Center at the Port of Los Angeles. The course was to have been held on March 21 – 23, 2012, however, the center did not receive sufficient enrollment to support the delivery of the course. CSR has set forth plans however, to offer the course in the early spring of 2013, at the Stevens Institute of Technology campus location in Washington, DC.

The objective of the three-day Port Security Sensing Technologies course is to provide maritime industry and homeland security practitioners with a basic understanding of the capabilities and limitations of sensor technologies used in maritime and port security applications. The course is designed to provide participants with a basis to make informed managerial decisions regarding relevant technology-based solutions.

Student participants learn the principal aspects of HF Radar, Satellites, Acoustics and Electro-Optics, as they are used in near shore and over the horizon vessel detection, tracking and classification.

Past course assessments have shown that the Port Security and Sensing Technologies course has been an invaluable conduit for dialog and collaboration between CSR researchers and the center’s end-users/stakeholders.

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The Port Security Sensing Technologies course can be taken for graduate credit leading to a Graduate Certificate in Maritime Security or a Master’s Degree in Maritime Systems from Stevens Institute of Technology, or for professional development purposes for 2 continuing education units (CEU’s). Students completing the course for graduate credit must complete a final course project in consultation with Stevens Maritime Systems faculty.

CSR continues to explore opportunities to collaborate with other DHS COE’s and is currently in discussion with representatives from the National Consortium for the Study of Terrorism and Responses to Terrorism (START) to develop a maritime security- training module as part of that center’s very successful Professional Training program.

Within the next year, CSR will adapt Stevens Maritime Systems curriculum and lectures from its co-sponsored seminar series, to develop a relevant module for inclusion in the START professional training program.

1.2. Maritime Security Graduate Certificate program

Enrollment in Stevens Institute of Technology’s Maritime Security Graduate Certificate program continued to grow in 2011. During the 2011-2012 academic year, six students were completing course work in the Maritime Security Graduate Certificate held on- campus at Stevens. This reflects an increase of two students from the previous year.

While enrollment in the Certificate program appears small, students from other degree programs enroll in the programs’ courses. Many students take one or two courses out of the four-course sequence to fulfill their elective course requirements, or for personal or professional reasons.

Enrollment in the Certificate program is expected to increase as recruitment expands beyond the fellowship students. The on-line component is convenient and flexible for many students and we expect on-line participation to grow as more students learn of it. The curriculum also continues to evolve to retain maximal relevance and inclusion of current practitioners as guest speakers. We also are working to position and articulate Maritime Systems as a recognizable program of study.

The Maritime Security Graduate Certificate is delivered on-campus at Stevens Institute of Technology in Hoboken, NJ and is offered completely online via Stevens WebCampus.

Students enrolled in the Maritime Security courses have included representatives from the US Navy and the USCG, and currently includes students in the Maritime Systems Master’s Degree Fellowship program.

The Maritime Security Graduate Certificate includes the following four courses:

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• OE 529 Maritime Safety and Security • OE 560 Fundamentals of Remote Sensing • OE 628 Technologies for Maritime Security • OE 629 Advanced Maritime Security

1.3. Maritime Systems Master’s Degree Fellowship Program – DHS Career Development Grant Awards

Figure 1. Maritime Systems Fellowship Student Recruitment Flyer.

Stevens Institute of Technology with the assistance of the CSR’s director of education has been awarded two consecutive DHS S&T Career Development Grant (CDG) awards. The 2010 and 2011 awards provide for full-tuition support and stipends for six full-time Master’s degree students pursuing a Master of Science in Maritime Systems with a Graduate Certificate in Maritime Security. Following completion of the fellowship program, students are required to engage in a minimum of one-year employment in the maritime homeland security domain.

Through a highly competitive and rigorous review process, Stevens granted three fellowship awards in 2011 and recently conferred three additional awards for students starting their studies in June 2012.

The initial fellowship recipients included Brandon Gorton, Christopher Francis and Danielle Holden. The students were selected based on their academic excellence and qualifications and their stated commitment to working within the homeland security domain following their academic tenure.

The fellowship program requires that students participate in the following activities during 24-month fellowship program:

• Students must maintain full-time enrollment in the Master's Degree program.

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• Complete required courses for the Master's Degree in Maritime Systems and the Graduate Certificate in Maritime Security, including a six-credit Master's Thesis. • Engage in a maritime security relevant10-week summer research internship in the field. • Attend at least one DHS Science & Technology (S&T) Education & Career Fair. • Participate in Stevens career development workshops and career fairs. • Participate in the CSR's eight-week Summer Research Institute. • Work closely with an assigned Stevens faculty advisor/mentor.

CSR’s director of education, Beth Austin DeFares serves as the program coordinator for the DHS-funded fellowship program, and Dr. Barry Bunin, Research Professor and Chief Architect, Maritime Security Laboratory serves as the academic advisor.

1.3.1. Maritime Systems Fellowship Recipients – DHS Career Development Grant (CDG) 2010 Award

Figure 2. 2010 Maritime Systems Fellowship Students, (L to R) Chris Francis, Danielle Holden, and Brandon Gorton.

Brandon Gorton joined the Maritime Systems fellowship program in Spring 2011 and is in the final semester of his fellowship program. Brandon received a Bachelor of Science degree in Engineering Management Technology from Western Michigan University in 2010 and with a cumulative GPA of 3.9. During his undergraduate studies, Brandon was a DHS Scholar student and an HS-STEM summer intern at Savannah River National Laboratory (SRNL), in the Global Security Section: Marine Group.

During the summer of 2012, Brandon worked as senior level summer intern at the Naval Undersea Warfare Center (UNWC) in Newport, RI. NUWC is a shore command of the U.S. Navy within the Naval Sea Command (NAVSEA) Warfare Center enterprise, which engineers, builds and supports America’s fleet of ships and combat systems. NUWC is the U.S. Navy’s research, development, test and evaluation (RDT&E) engineering fleet support center for submarine warfare systems and other systems associated with the undersea battle-space.

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During his 10-week internship, Brandon worked in direct collaboration with NUWC’s engineers and scientists. His internship responsibilities included the development of a security-architecture for an Acoustic Impulse Device (AID) and the deployment of the device in the test waters off Newport, RI. The AID is designed to deter swimmers from in and around critical infrastructure areas.

Brandon’s experience at NUWC will contribute to the final preparation of his Master’s thesis in the area of acoustic detection of underwater threats. Brandon will complete his Master’s degree in Maritime Systems in December 2012 and is currently applying for employment opportunities within NUWC and the Homeland Security domain.

Christopher (Chris) Francis completed his degree requirements for a Bachelor of Engineering in Naval Engineering at Stevens Institute of Technology in May 2011. Chris maintained a cumulative GPA of 3.9 and ranked 9th in his undergraduate class of 499. Chris joined the Maritime Systems Master’s Degree Fellowship program in June 2011.

As an undergraduate student, Chris worked on a Senior Design project focused on the forensic and engineering analysis of semi-submersible vessels to assist law enforcement in the interdiction of such vessels used by drug traffickers. Throughout his Senior Design project, Chris collaborated with Stevens engineering faculty and CSR affiliate faculty and researchers.

During the summer of 2011, Chris participated in the CSR Summer Research Institute and served as a team leader for the Modeling and Simulation sub-team within the Consequence Assessment and Management Team. Chris and his team successfully developed a new web interfaced called Magello. Magello combines multi-source data into one user-friendly interface for the purpose of providing first responders and decision- makers with critical environmental and atmospheric information needed during emergency and crisis situations.

In June 2012, Chris engaged in a 10-week internship at the Naval Undersea Warfare Center (NUWC) in Newport, RI, together with Brandon Gorton. Chris’ internship responsibilities included the development of 3D drawings for a proposed Integrated Swimmer Defense (ISD) system for inclusion in a prospective client report. Chris also worked shoulder to shoulder with NUWC engineers to assist in the deployment of the ISD system in the test waters off of Newport, RI.

Chris will leverage his experience and professional contacts at NUWC to refine his Master’s thesis on the detection and interdiction of fully submersible vessels. Chris is expected to complete his master’s degree and fellowship program in May 2013.

Danielle Holden completed her Bachelor of Science degree in Marine Sciences and Physical Oceanography, at Rutgers University in May 2011, and joined the fellowship

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program in June 2011 as a team leader and participant in the CSR 2011 Summer Research Institute.

Throughout her undergraduate degree program, Danielle had conducted cutting-edge research in collaboration with faculty from Rutgers University’s Coastal Ocean Observation Laboratory (COOL). In 2009, Danielle participated in the COOL Lab’s successful transatlantic deployment of an underwater glider from the shores of New Jersey to the coast of Spain. The first voyage of its kind by an underwater glider, the Slocum glider (RU27) is now on display at the Smithsonian in Washington, DC. Through her work conducted on the RU27, Danielle also engaged in the North America-Norway educational program (NORUS). Leveraging her research in the area of High Frequency (HF) radar, Danielle earned a trip to Norway where she worked with an international team of student researchers to conduct marine monitoring and ocean observation experiments.

In the summer of 2010, Danielle was selected to participate in the CSR’s Summer Research Institute where she served as a team member on the HF radar student research team assessing the capabilities and limitations of HF radar on the detection of small vessel threats in the Hudson River Estuary opposite of New York City. Danielle’s accomplishments during the SRI earned her recognition at the 5th Annual DHS University Network Summit and at the 2011 American Society of Limnology and Oceanography (ASLO) meeting in San Juan, PR, where she provided a presentation on her teams’ summer research findings.

Currently in her second year of the Maritime Systems Fellowship program, Danielle engaged in a 10-week internship at the Naval Surface Warfare Center (NSWC) – Carderock facility, in Bethesda, MD. Danielle worked along side other Stevens alumni currently employed at Carderock, to conduct a risk assessment of Liquefied Natural Gas (LNG)vessels bunkering at terminals.

Danielle culminated her field experience by preparing a formal report and presentation for NSWC engineers and scientists on her summer project. Danielle plans to leverage her professional contacts and experience at Carderock to develop her Master’s thesis on the risks of LNG vessels at port and the cyber security vulnerabilities of their control systems. Danielle is expected to complete her degree and fellowship program in May 2013.

A Stevens Institute of Technology news article entitled, Stevens Educates the Next Generation Maritime Security Leaders, featuring the three 2010 DHS CDG Fellowship students can be found on the Stevens website at: http://www.stevens.edu/news/content/stevens-educates-next-generation-maritime- security-leaders

Summary of DHS CDG 2010 Fellowship Student Activities and Accomplishments 143

Table 2. Summary of DHS CDG student activities – 2010 Grant Award. Student Program Program Activities Outcomes and Start Accomplishments Chris Francis June 2011 -Team leader & -Completed a 10-week field participant SRI internship at NUWC. Worked 2011. on the development of 3D -Engaged in drawings for a proposed regularly scheduled Integrated Swimmer Defense meetings with (ISD) system. Faculty Advisor & -SRI 2011 team research Fellowship paper, presentation and Coordinator. poster. -Beginning work on -Led the development of a Master’s Thesis. new web portal designed to -Attended Stevens provide critical air/sea data Career Fairs in Dec. for emergency responders 2011 & March 2012. during crisis situations. - 2012 summer -Utilized the new Magello internship at the web interface to provide Naval Undersea technical assistance and spill Warfare Center modeling to the NY City (NUWC) in Dept. of Environmental Newport, RI. Protection during a July 2011 sewage spill in the Hudson River. -Demonstrated Magello at the 2011 USCG Innovation Expo. -Presented research work to CSR SEAC board members. -Presented research and attended meetings with representatives from the USCG, Regional Catastrophic Planning Team (RCPT), NOAA, Port Authority of NY & NJ (PANYNJ), Maersk, U.S. Navy, NY Office of Emergency Management (OEM), CSR & DHS S&T. Brandon January -Team leader and Completed 10-week field Gorton 2011 participant SRI internship at NUWC. 2011. Developed security

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-Conducting work architecture for an Acoustic on Master’s thesis in Impulse Device (AID). the area of acoustic -SRI team research paper, detection of vessel presentation and poster. threats. -Assisted in the development - Engaged in of a new graphical user regularly scheduled interface & information meetings with system that can be used to Faculty Advisor and identify vessel traffic Program anomalies in the New York Coordinator. Harbor. -Attended Stevens -Presented research and Career Fairs in Dec. attended meetings with 2011 & March 2012. representatives from the - Participated in a USCG, PANYNJ, U.S. Navy, summer internship at Lockheed Martin, NY Office the Naval Undersea of Emergency Management Warfare Center in (OEM), CSR & DHS S&T. Newport, RI. -Laboratory tour guide for visiting VIP’s and guests to Stevens. Danielle June 2011 -Team leader and -Completed 10-week field Holden participant SRI internship at NSWC. 2011. Conducted risk assessment of -Fulltime enrollment LNG vessels bunkering at Fall 2011 and Spring terminals. 2012. -SRI team research paper, -Beginning work on presentation and poster. Master’s Thesis. -Assisted in the development - Engaged in of a new graphical user regularly scheduled interface and information meetings with system that can be used to Faculty Advisor & identify vessel traffic Program anomalies in the New York Coordinator. Harbor. -Attended Stevens -Prepared and presented a Career Fairs in Dec. term paper to the PANYNJ on 2011 & March 2012. the risk assessment of the -2012 summer PANYNJ’s environmental internship at the stewardship of the Port of Naval Surface Elizabeth. Warfare Center -Presented research and (NSWC), Carderock attended meetings with

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facility, Bethesda, representatives from the MD. USCG, PANYNJ, U.S. Navy, Lockheed Martin, NY Office of Emergency Management (OEM), CSR & DHS S&T.

In the fall of 2011, Stevens was awarded its second DHS CDG award. With this award, Stevens conducted another extensive student recruitment campaign and through a competitive review process, offered fellowship awards to three outstanding candidates. These students began the fellowship program in June 2012. The three students are Abdul Jalloh, from City College of New York, Alexander Pollara, from Stevens Institute of Technology, and Grace Python, from Stevens Institute of Technology.

1.3.2. Maritime Systems Fellowship Recipients – DHS Career Development Grant (CDG) 2011 Award

Figure 3. 2011 Maritime Systems Fellowship Students, (L to R) Alex Pollara, Grace Python, and Abdul Jalloh.

Abdul Jalloh completed his Bachelor of Engineering degree in Electrical Engineering from The City College of the City University of New York. He joined City College through the New York state-funded Search for Education, Elevation and Knowledge (SEEK) program. Throughout his undergraduate academic career, Abdul had been a student researcher in the Optical Remote Sensing Laboratory within the NOAA CREST Institute at The City University. Funded through a grant by the Louis Stokes Alliance for Minority Participation (LSAMP), his research focused on monitoring and understanding atmospheric flows in the NYC urban area.

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In June 2012, Mr. Jalloh began his fellowship requirements in the CSR’s Summer Research Institute. Due to unforeseen circumstances, Abdul is currently on leave from the fellowship program.

Alexander Pollara completed his Bachelor of Engineering degree in Naval Engineering at Stevens Institute of Technology in May 2012. During his freshman and sophomore years, Alex was awarded a Stevens Technogenesis Undergraduate Summer Scholarship to conduct research in Stevens Davidson Laboratory. During his scholarship program, Alex worked on a U.S. Army-funded project that tested Advanced Amphibious Vehicles (AAV) and a new Marine Personnel Carrier (MPC). His research experience as a Technogenisis Scholar later earned him a Naval Research Entrepreneur Internship Program (NREIP) summer internship at the Naval Surface Warfare Center (NSWC), Caderock facility, in Bethesda, MD.

Alex’s internship and research experiences helped to shape his interest in maritime security. During his first semester in the Maritime Systems fellowship program, Alex engaged in the CSR’s Summer Research Institute, as a team member on the Acoustic Sensor Technologies team. Alex’s team explored the use of Machine Learning to assist engineers in the automatic detection and classification of vessels.

Grace Python joined the Maritime Systems Fellowship program in June 2012, following the completion of her Bachelor of Engineering degree in Civil Engineering from Stevens Institute of Technology. During her undergraduate studies, Grace took additional graduate-level courses in the area of maritime security, a passion she developed while attending a youth military academy in high school.

Grace’s coursework and leadership in her graduate-level courses gained her the recognition and attention of Stevens’ Maritime Systems faculty members. As the only undergraduate in a class of all graduate students, Grace achieved the highest assessment of an A+ on a mid-term research paper for her Safety and Security in Maritime Systems course. It is through her professors’ encouragement and strong recommendations that Grace applied for the Maritime Systems Master’s Degree Fellowship.

During the summer of 2012, Grace participated in the CSR’s Summer Research Institute and collaborated with researchers from MIT’s Center for Transportation and Logistics (CTL) to advance the scenario-based capabilities of the center’s Port Mapper Tool. Grace and her colleague on the Port Mapper Tool project, Javier Raviera from the University of Puerto Rico- Mayaquez, analyzed the impacts of port disruptions and closures on intermodal connections.

Summary of DHS CDG 2011 Fellowship Student Activities and Accomplishments

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Table 3. Summary of DHS CDG student activities – 2011 Grant Award. Student Program Background Outcomes and Start Accomplishments Abdul June 2012 B.Eng, in Electrical -SRI 2012 participant on the Jalloh* Engineering from Magello team. The City College of -Student researcher in the *Abdul is the City University of Optical Remote Sensing Lab currently on New York, May at City College leave from 2012. -Completed research on the atmospheric monitoring in the fellowship NY/NJ urban environments. program. -Presented research at the national Louis Stokes Alliance for Minority Participation conferences at Brookhaven National Lab and Einstein in the City conference. -Co-author of a paper published and presented at the 2012 SPIE Defense, Sensing and Security Conference in Bethesda, MD. Alexander June 2012 B.Eng in Naval -Participated in the SRI 2012 Pollara Engineering from program and served as a team Stevens Institute of member on the Acoustic Technology, May Sensor Technologies Team. 2012. -Attended field visits to CBP and the USCG and conducted experiments in the MSL Lab. -Stevens Technogenesis Scholarship recipient. -Member Stevens Honor Board. - Participant in a NREIP internship at the Center for Innovation in Ship Design at the US Navy’s Carderock facility. Grace Python June 2012 B.Eng in Civil -Participated in the SRI 2012 Engineering from program and served as a team Stevens Institute of member on the Port Mapper Technology, May Tool team.

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2012. -Attended field visits to CBP and the USCG. -Completed research on NYC sewage spill in the Hudson River, resulting in the highest mid-term grade in a graduate level class. -Distinguished as a high- potential student by Stevens Maritime Security faculty members.

1.4. Summer Research Institute (SRI) 2012: End-user Engagement and Technology Transition

Figure 4. SRI 2012 program brochure.

CSR held its third annual Summer Research Institute, from June 4 to July 27, 2012 at the Stevens Institute of Technology campus in Hoboken, NJ.

Lessons learned during the 2010 and 2011 SRI programs served as the framework for the third annual summer research program. Student and faculty feedback received from the previous years’ programs have taught CSR administrators that to maximize the professional development of students and to facilitate innovative research outcomes, students must spend more time participating in real-world fieldwork and experiments, than in lectures in the classroom.

Through funding support from external sources and existing scholarship and fellowship programs, CSR was able to admit ten high-potential students into the 2012 Summer Research Institute. Altogether, four students participated from Stevens Institute of Technology utilizing Stevens scholarship and fellowship funding, and five students from the University of Puerto Rico-Mayaguez attended with support from CSR’s sister 149

research center, CIMES, and the university’s 2011 DHS Scientific Leadership Award. Only one student from Drexel University received funding through CSR to attend the program.

The student participants represented a broad range of science and engineering disciplines including, Computer Science, Computer Engineering, Electrical Engineering, Maritime Systems, and Mechanical Engineering. Seven of the students were undergraduates, while three were Master’s degree students.

1.4.1 SRI 2012 Program Theme

Figure 5. Bradford Slutsky, Program Manager Officer, CBP, hosts SRI 2012 students on-site at Port Newark for a real-world look into CBP operations and security procedures.

The theme of the third annual SRI was End-user Engagement and Technology Transition. This year’s program was designed to build upon prior SRI initiatives and current CSR research to advance the innovative capabilities and functionality of the center’s existing and evolving tools and technologies for transition to end-users (e.g. USCG). Student projects included hands-on, multi-disciplinary research activities to maximize the situational awareness, emergency response, and maritime surveillance capabilities of first responders and emergency management personnel. Students were encouraged to develop creative solutions and new maritime security applications, leveraging CSR tools and technologies.

Students engaged in lectures by CSR researchers as well as industry and government homeland security practitioners, to develop an understanding of the complex issues in maritime domain awareness, the Marine Transportation System, emergency response, maritime system resilience, and the sensor tools and technologies that can enhance situational awareness and surveillance capabilities. 150

Students were assigned to one of the following four project teams: • Acoustic Sensor Technologies • Magello Emergency Response Tool • Port Mapper Tool • HF Radar The program activities and exercises challenged students to consider alternative uses of visualization, modeling tools for emergency planning purposes and sensor technologies (HF Radar, Acoustics, Electro-Optics, etc.) for incident analysis and forensic/attribution purposes.

1.4.2 SRI Guest Speakers

Students received real-world insight into the state and practice of maritime security and the Marine Transportation System through presentations and lectures by CSR researchers and maritime industry and homeland security experts. This past summer’s guest speakers included:

• Dr. Nabil Adam, DHS S&T Critical Infrastructure and Disaster Management Division • Mr. Kevin Brenker, Director of Business Development, Symetrica • Chief Kevin McCabe, Anti-Terrorism Contraband Enforcement Team (A-TCET), Customs and Border Protection (CBP) • Ms. Sharon McStein, Manager, Industry & Government Relations, Port Commerce, Port Authority of New York and New Jersey (PANYNJ) • Mr. Brian Onieal, Manager, NJ Office of Homeland Security and Preparedness (NJHSP) • Mr. Nick Pera, Chief Systems Engineer, U.S. Navy 1.4.3. Field Visits and Meetings with Practitioners

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Figure 6. SRI 2012 students met with Deputy Chief Rudy Frank, Anti-Terrorism Contraband Enforcement Team (A-TCET), Customs and Border Protection (CBP) during a field visit to CBP facilities at Port Newark.

Field visits and interactions with industry practitioners are a key facet of the Summer Research Institute. Visits and discussions with practitioners provide students with a contextual framework and practical insight into the real-world implications of their SRI projects and research. During the eight-week program, students had the unique opportunity to participate in field visits to Customs and Border Protection (CBP) facilities at Port Newark, U.S. Coast Guard Fort Hancock, and the Fire Department of New York - Marine Battalion (FDNY), in New York City.

Student interactions with CSR stakeholders have yielded invitations for SRI students to engage in emergency planning and preparedness exercises facilitated by the US Coast Guard Sector New York and the New Jersey Office of Homeland Security and Preparedness.

1.4.4. Student Participation Criteria

Participation in the SRI requires that students be currently enrolled in an undergraduate of graduate level science or engineering degree program. Undergraduate students must possess a minimum GPA of 3.0, and graduate level students are required to have a GPA of 3.5 or better.

The student selection for the SRI 2012 program was conducted by the organization providing funding support for the student’s participation in the program. All but one of this year’s participants attended the SRI through a corresponding Fellowship, Scholarship and/or Leadership Award program. Table 4 below identifies the students participants enrolled in the 2012 SRI.

Table 4. SRI 2012 Student Participant List SRI 2012 STUDENT PARTICIPANT LIST

UNIVERSITY STUDENT MAJOR & DEGREE Drexel University Timothy Higgins Computer Science / Undergrad.

Stevens Institute of Abdul Jalloh Maritime Systems/Grad. Technology Blaise Linn Mechanical Eng./ Alex Pollara Undergrad. Grace Python Maritime Systems/Grad. Maritime Systems/Grad

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University of Puerto Rico Raul A. Huertas-Avil Electrical Eng./ Undergrad. - Mayaguez Isaac Jordan Forty Electrical Eng., Undergrad. Javier Rivera Collazo Electrical Eng., Undergrad. Nicole Rodriguez Carrion Electrical Eng., Undergrad. Fernando Valverde Valle Electrical Eng., Undergrad.

1.4.5. Student Funding and Support

Students received summer stipends of up to $4,000 and were provided accommodations at on-campus at Stevens Castle Point Apartments. Student participants residing outside of New Jersey and the metropolitan area were also provided with up to $1,000 in travel reimbursement to and from the summer research program.

Out of the 10 students admitted into the program, nine attended the program fully funded by external sources. Four of the students were Stevens students who attend the SRI based on Stevens fellowship and scholarship program support, three other students were supported (stipends, housing and travel costs) by the University of Puerto Rico- Mayaguez through its 2011 DHS Scientific Leadership Award, and two others were supported by our sister research center, the Center for Island, Maritime and Extreme Environment Security (CIMES).

1.4.6. Program Administration

The day-to-day administration and coordination of the SRI was a team effort supported by Ms. Beth Austin DeFares, CSR Director of Education, and Dr. Barry Bunin, Chief Architect, Maritime Security Laboratory, under the executive leadership of Dr. Julie Pullen, CSR Director.

Dr. Bunin served as the lead faculty facilitator and curriculum developer, and Ms. DeFares functioned as the primary program and student coordinator.

1.4.7. Program Format and Curriculum

The program format was designed to provide a balance between in-class lectures and experiential learning opportunities in the field. The program format also included professional development activities, whereby the students engaged in weekly research status updates in the form of oral presentations and power point slides.

The first two weeks of the SRI program were devoted to providing a contextual framework in which the group of students would begin to learn about the critical nature and complexity of port and maritime security. Faculty and guest lectures discussed the

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economic impact of the marine transportation system in global trade and to the US economy, and the significance of maritime domain awareness in helping to protect and secure our nation’s ports, inland waterways, and island and coastal borders. Lectures and field visits also introduced students to the various private and government stakeholders in the maritime and port security domain.

During Week One of the program Dr. Thomas Wakeman, Professor Maritime Systems, Stevens Institute of Technology, provided students with a series of introductory MDA and MTS lectures. Given that the students came from a broad range of academic disciplines and experiences, Dr. Wakeman’s lectures were designed to provide students with an understanding of the maritime domain and the various public, private, federal, state and local stakeholders involved in the Marine Transportation System. A guest presentation by Mr. Brian Onieal, New Jersey Office of Homeland Security and Preparedness (NJHSP) provided the students with a homeland security practitioner’s perspective into the real-world issues and complexities of securing the nation’s third largest port, the Port of Newark, and its arterial waterways.

During the latter part of the week, the students were given their team assignments and introduced to their respective research mentors. Throughout the remainder of the eight- week program, the students would begin to engage daily in their assigned research projects, in coordination with their Faculty Mentors.

In Week Two, students attended a series of lectures on the capabilities and limitations of sensor technologies used in port and maritime security applications, led by Dr. Barry Bunin, Chief Architect, Maritime Security Laboratory and Academic Advisor, Maritime Systems Fellowship program. During the week, Dr. Bunin, offered a seminar on the uses of Electro-Optics in maritime domain awareness and the properties of long-wave and short wave infrared imaging, and organized talks by Dr. Scott Glenn, Director, COOL Lab, Rutgers University, on the principals of HF Radar and its utility in the near-shore and over-the-horizon detection and tracking of vessels, and by Dr. Alexander Sutin, Research Professor, Stevens Institute of Technology, on sound waves and acoustic signatures, used to detect, track and classify in waterborne vessels.

Students sythesized what they had learned during the week, in a field exercise that required them to compare visual observations of vessels in the New York Harbor, with concurrent AIS and passive acoustic signatures gathered by Stevens Passive Acoustic Detection System (SPADES).

Students engaged in a comprehensive field visit to Customs and Border Protection (CBP) facilities at the Port of Newark, during Week Three. Facilitated by Bradford Slutsky, Manager Public Outreach, CBP, the students observed the real-time inspection of cargo containers as they exited the port and were given access to the CBP’s warehouse of confiscated items used to smuggle illicit drugs and counterfeit merchandise into the

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country. The students were given the unique opportunity to ask questions and to engage one-on-one with a broad base of CBP representatives.

During Week Four, the students had the unique opportunity to participate in a conference on the Impacts of Extreme Climate Change on Urban Environments. Hosted by Stevens Center for Maritime Studies, the conference included the nation’s leading researchers and academicians to discuss the effects of extreme weather on the safety and security of urban environments and coastal populations.

In Week Five, Nick Pera, Chief Systems Engineer, US Navy provided the students with first-hand in-sight into the US Navy’s strategies for securing and safeguarding the military’s high-value assets. The SRI students also had the opportunity to learn about supply chain disruptions and port resilience from CSR research PI, Jim Rice from the Center for Transportation and Logistics at Massachusetts Institute of Technology (MIT). At the end of Week Five, the students met with representatives from the Fire Department of New York Marine Battalion, and toured the department’s state-of-the art fire vessel, “343”.

During Week Six, the students discussed ocean and urban impacts on weather predictions and lessons learned from the Fukishima nuclear reactor incident, in a faculty lecture by Dr. Julie Pullen, Director, CSR.

In,Week Seven the students discussed their research projects with Dr. Nabil Adam, DHS S&T Critical Infrastructure and Disaster Management Division and learned about port operations from Sharon McStein, Manager, Industry & Government Relations, Port Commerce, PANYNJ.

During Week Eight, the students completed their final team research reports and presented their final team projects to CSR researchers, Stevens faculty and invited guests from CBP and NJHSP.

Table 4 below illustrates the detailed eight-week SRI schedule. Table 4. SRI 2012 Program Schedule

TOPIC FACULTY FACULTY/GUES SRI 2012 SCHEDULE LEADS T SPEAKERS ACTIVITIES

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Brian Onieal, New Faculty/Studen MDA/MTS Thomas Jersey Office of t introductions WEEK ONE Industry Wakeman, Homeland & tour of June 4 - 8 Overview Stevens Inst. of Security Stevens Technology research labs & facilities Barry Bunin – Acoustic and Electro-Optics AIS WEEK Barry Bunin, Sensor Scott Glenn – HF observation TWO Stevens Inst. of Technologies Radar experiment – June 11 - 15 Technology Alexander Sutin- 6th Floor Patio Acoustics – Babbio Ctr. Kevin Brenker, Symetrica – Radiation Field Visit to Detection CBP Port WEEK Technologies Newark Team Research THREE Projects June 18 - 22 Kevin McCabe – Status Update Anti-Terrorism presentations Contraband Enforcement Team, CBP Extreme Climate WEEK Change Team Research FOUR Conference Projects Jun. 25 – 29 Status Update Presentations Jim Rice, MIT Field Visit to WEEK Supply Chain Nick Pera, Chief FDNY FIVE and Port Systems Engineer, Radiation July 2 - 6 Resilience US Navy Detection Vessel - NYC

WEEK SIX Status Update Project Time July 9 - 13 Presentations

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Field Visit to Nabil Adam, DHS USCG Ft. S&T Critical Hancock – WEEK Infrastructure Sandy Hook, SEVEN Project Time Division NJ July 16 - 20

Sharon McStein, Status Update PANYNJ Presentations Program Brad Slutsky, CBP WEEK Final Team Synthesis Final EIGHT Presentations team repots & Brian Onieal, July 23 - 27 presentations NJHSP

1.4.8. Student Teams and Research Projects

SRI administrators organized the student research teams according to student interest and to reflect a reasonable balance between school representation, degree status, and academic discipline. The HF Radar team however, was organized so that three of the five University of Puerto Rico – Mayaguez students, could leverage their SRI research and experience to advance and help build-out the Center’s Caribbean test bed in Puerto Rico.

Each of the four student teams were challenged to build upon prior SRI initiatives and current CSR research to advance the innovative capabilities and functionality of the center’s existing and evolving tools and technologies for transition to end-users (e.g. USCG). Student projects included hands-on, multi-disciplinary research activities to maximize the situational awareness, emergency response, and maritime surveillance capabilities of first responders and emergency management personnel. Students were encouraged to develop creative solutions and new maritime security applications, leveraging CSR tools and technologies. . The student teams had at their disposal a robust set of research assets including a collection of sensor technologies, access to analysis and visualization tools, plume modeling and simulation tools and unprecedented access to CSR researchers and leaders and experts in the field of maritime and homeland security.

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Figure 7. SRI students compare Acoustic data to AIS and visual observations of vessels in the NY Harbor from CSR’s 6th floor patio of the Babbio Center at Stevens Institute of Technology.

1.4.8A. Acoustic Sensor Technologies

Students leveraged CSR’s Maritime Security Laboratory (MSL) sensor suite, including AIS, Infrared and Visible Light cameras, acoustic sensors, as well as Rutgers HF Radar to track and classify vessels in the New York Harbor.

In their investigation into the use of passive acoustic signatures for vessel classification, the students employed a computational approach based on machine-learning algorithms to automatically identify vessel signatures. The Acoustic Sensor Technologies student team developed a method to extract several key features of passive acoustic signatures, by testing different Machine Learning Algorithms.

Based on their research and assessments, the students found that it was possible to sort vessels according to their demodulated acoustic signature using a variety of machine learning algorithms. In addition, the students found that the algorithms were able to extract and identify generalized acoustic traits from a crowded input signal.

The student’s findings indicated that there is significant potential for the successful use of Machine Learning Algorithms in passive acoustic based vessel identification.

Details regarding the Acoustic Sensor Technologies Team project outcomes can be found in their final presentation slides located on the CSR’s Summer Research Institute website at http://www.stevens.edu/csr/education/SRI-2012.html.

Acoustic Sensor Technologies - Student Team members: Student Academic Discipline School Blaise Linn Mechanical Stevens Inst. of Technology 158

Engineering Alex Pollara Maritime Systems Stevens Inst. of Technology Nicole Rodriguez Electrical Engineering Univ. of PR - Mayaguez Faculty Mentors: Drs. Barry Bunin and Alexander Sutin, Stevens Inst. of Technology

1.4.8B. Magello Emergency Response Tool

The Magello Emergency Response Tool team was challenged to advance the functionality of the Magello web interface from prototype phase, into an operational, real-time emergency response tool. Much of the Magello team’s work, focused on understanding end-user needs and refining the system to accommodate the situational awareness needs and modeling capabilities of the web tool.

Timothy Higgins, from Drexel University, served as the primary programmer for the Magello website. He spent numerous hours refining atmospheric and oceanic data feeds, so they could be archived and processed without system delays to the end-user. He also worked to provide uniformity to the look and feel of Magello, to ensure that the site was easy to navigate and intuitive for emergency responders.

Through Tim’s efforts, he was able to program the site to provide a timeline of data feeds that would provide the end-user with a history of observations, together with real-time observations and model forecasts.

The Magello team’s efforts will be showcased by Dr. Julie Pullen, CSR Director, at a FEMA Region 1 Technology Transition Workshop being held in late September 2012 in Boston, MA.

For additional information about the Magello team project outcomes, please review their final presentation slides at http://www.stevens.edu/csr/education/SRI-2012.html

Magello Emergency Response - Student Team members: Student Academic Discipline School Timothy Higgins Computer Science Drexel University Abdul Jalloh Maritime Systems Stevens Inst. of Technology Faculty Mentors: Drs. Julie Pullen and Philip Orton, Stevens Inst. of Technology

1.4.8C. Port Mapper Tool

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The Port Mapper Tool student research team worked to develop port closure scenarios to enhance the eventual modeling and simulation capabilities of MIT’s Center for Transportation and Logistics (CTL) Port Mapper tool. While still in prototype phase, the tool is being designed to assist policy makers in developing response and resiliency plans in the event of port and supply chain disruptions. The tool will allow end-users to conduct scenario-based analysis on the implications and repercussions of disruptions and closures of US ports.

During the SRI 2012, the Port Mapper Tool team assessed the vulnerabilities of the food and farm supply chain, in the event of a port closure or disruption to the Port of New Orleans and its companion ports in the New Orleans metropolitan area. A scenario was developed in order to show the primary vulnerabilities of this geographical area and how the movement of food and farm cargo would be affected. The chain of events due to a port closure at the Port of New Orleans would also put into jeopardy the regions three arterial ports, requiring shippers and distributers to reroute the food and farm cargo to other US ports, who have the capacity and relevant facilities to absorb such goods. Intermodal connections and infrastructure are limiting factors in the amount of goods that a port can handle. For a short period of time it is possible that other ports would be able to absorb the volume of the disrupted port, but it is necessary that the affected port be brought back to full capacity as soon as possible or it risks losing revenue and permanent business.

By utilizing a scenario based assessment to a analyze the impacts and complexities of port disruptions to the supply chain flow of goods, the student research team was able to gather detailed information regarding available intermodal connections. This data can be used as critical input in the further development of the Port Mapper Tool, providing end- users with a better understanding of the impact of disruptions in a port and therefore making resiliency plans based on the tool more effective.

In a unique twist of fate, much of what the students chronicled in their intermodal connection scenario,, came to fruition this summer, when a severe drought in the mid- west crippled the flow of the Mississippi River and limited the throughput of food and farm commodities for export.

For additional information about the Port Mapper Tool team project outcomes, please review their final presentation at http://www.stevens.edu/csr/education/SRI-2012.html.

Port Mapper Tool - Student Team members: Student Academic School Discipline Javier Raviera Collazo Electrical Univ. of PR - Mayaguez Engineering Grace Python Maritime Systems Stevens Inst. of Technology

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Faculty Mentors: Jim Rice and Kai Trepte, MIT Center for Transportation & Logistics

1.4.8D. HF Radar Team

Three students from the University of Puerto Rico-Mayaguez (UPRM) were selected to participate on the HF Radar Team under the mentorship of Drs. Scott Glenn and Hugh Roarty from Rutgers University. The student team was purposefully organized with the intention that the students would later return to the University of Puerto Rico and continue their research in collaboration with Drs. Jorge Corredor and Miguel Canals, CSR academic partners from UPRM, to advance the CSR’s Caribbean HF Radar test bed in Puerto Rico.

For this purpose, the student’s summer research project was to understand the dual- capabilities of HF Radar for surface current tracking and for vessel detection, and then to develop algorithms to increase the probability of vessel detections and to lower the false alarm rate.

Through their research and field experiments, the students found that there was a tradeoff between the range and the spatial resolution of the radar. With higher frequency there is more spatial resolution but less range, and in contrast, with lower frequency there is less spatial resolution and more range. They also found that the major advantage of using HF Radar is the over-the- horizon capabilities of the radar to detect and track vessels.

The students identified several challenges in using HF Radar to detect vessels in harbors and busy port areas.. The main weakness the students found was the inconsistency of the radar to detect small vessels and the camouflaging of detection signals when a vessel was transiting perpendicular to the radar antenna.

To resolve these issues, the students argued that a network of coastal radars operating at high frequencies could provide increased surveillance capabilities and improved detection rates.

To learn more about the HF Radar Team project and to review their final presentation slides, please visit the CSR website at http://www.stevens.edu/csr/education/SRI- 2012.html

HF Radar - Student Team members: Student Academic Discipline School Raul Huertas-Avil Electrical Univ. of PR -Mayaguez Engineering 161

Isaac Jordan Forty Electrical Univ. of PR-Mayaguez Engineering Fernando Valverde Electrical Univ. of PR - Mayaguez Valle Engineering Faculty Mentors: Drs. Scott Glenn and Hugh Roarty, Rutgers University

1.5. SRI Assessment

An assessment of the SRI 2012 program outcomes and lessons learned was conducted at the end of the eight-week summer research program in the form of a student survey. Feedback from the survey responses, together with the student team project outcomes, research reports and presentations, all indicate that the SRI was effective in meeting its program objectives and successful in creating positive research experiences and experiential learning opportunities for its students.

1.5.1 Student Survey Results

Nine out of the ten program participants completed the SRI student survey. The intent of the survey was to assess the impacts of the SRI’s curriculum, faculty mentorship and field activities on the learning outcomes of the students and their enhanced interest in pursuing continued academic study and/or a career in the homeland security domain.

Overall, the student respondents rated the SRI “Excellent” in the following areas: : • Faculty Lectures • Faculty Mentor Guidance and Assistance • Ability to be innovative and Self-Motivated • Quality of the Program Curriculum • Quality of Research Facilities • Quality of Program Administration and Coordination And “Very Good” in the following areas: Quality of teamwork, Field trips, Guest lectures and Research outcomes.

When asked to what extent the SRI enhanced or improved their professional skills, a majority of the students said that the program “Effectively Improved” their skills in the following areas: Communications, Leadership, Professional confidence, Oral presentations, Writing, Networking and Teamwork/collaboration. The students rated the SRI “Excellent” in enhancing their “ability to conduct research”.

In assessing the students learning outcomes in the area of sensor technologies and maritime domain awareness, a majority of the students said that the SRI “substantially improved their knowledge” of Port Operations, Port Security and Acoustics used in vessel detection and classification, and “adequately provided working knowledge” of the 162

capabilities and limitations of HF Radar, Emergency Management and Response, Supply Chain and Port Resilience, Port Operations and Port Security.

When asked to identify the top 3 takeaways from the SRI, students commonly mentioned the following items: • Team work and the diversity of the students in the SRI. • Improved communication and oral presentation skills • Knowledge and experience • Motivation and independent research When describing the weaknesses of the program, a majority of the students commonly said: • Time constraints and too little time to complete research projects. • Not enough field experiments. • Not enough time with faculty mentors. • Inability to conduct on-the-water field exercises When asked how they would best describe their experience in the SRI, some students wrote: • :It has been a great experience that will definitely enhance the possibility of further research in the maritime domain awareness field.” • “Extremely educational; it opened me up to areas I probably wouldn't have otherwise considered for the future.” • “My experience during this program was awesome. I learned a lot of maritime security and port security. I improved my personal communication skills, which is a great thing because engineers have to communicate in a way that anybody can understand what we are talking about.”

1.5.2 SRI 2012 Outcomes and Lessons Learned

The outcomes generated by this summer’s research program helped to advance the innovative capabilities and functionality of the center’s existing and evolving tools and technologies.

Each of the projects completed during the 2012 summer research program, have the potential to enhance the maritime domain awareness and security capabilities of maritime security professionals, emergency management personnel and homeland security decision makers.

The students worked in close collaboration with CSR researchers and had the unique opportunity to interact and engage with real-world industry and government maritime and homeland security leaders and practitioners.

Overall, the SRI was effective in achieving the following outcomes:

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• Student research projects resulted in the advancement of tools and technologies that can be used to assist in maritime domain awareness and enhance the security capabilities of maritime security practitioners, first responders and homeland security decision makers. • Student research reports, field experiments and weekly presentations demonstrated the student’s knowledge and understanding of the marine transportation system and maritime domain awareness. • Students enhanced their professional skills by providing weekly research status updates and oral presentations. • Students developed strong professional bonds and friendships with each other. • Students expressed enhanced interest in pursuing careers and/or advanced academic study in maritime/homeland security as a result of their participation in the SRI.

Lessons learned in this year’s program demonstrate that student research outcomes are positively affected by the following factors: the amount of time spent conducting research, collaborations and engagement with CSR researchers and industry and government practitioners, and access to real-time, state-of-the art tools ands technologies.

1.5.3. Recommendations

The following recommendations for the future delivery of the SRI take into consideration conversations held between CSR faculty and administrators, and specific comments received in the student survey responses.

Program Format: While CSR made a number of modifications to the 2012 SRI program format, administrators should continue to find ways to engage students in their research and field experiments earlier on it the program. Recommendations have been made to engage students in their research from the start of the program and to extend the program beyond the eight-week timeframe.

Faculty Mentorship: Some students recommended that faculty mentors schedule consistent weekly meetings with their teams and provide more input into the direction of the teams’ work.

1.5.4. Conclusion

Lessons learned in the previous years’ SRI programs provided a strong framework for the 2012 summer research program. Modifications to the program format to allow for increased student research-time positively influenced the 2012 student research outcomes.

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Collaborations in and among the student teams and the CSR faculty and administrators, and the involvement of real-world industry and government homeland security practitioners continue to be the key features of the SRI program.

Students were provided with unprecedented opportunities to interact with representatives from the following organizations and federal agencies: CBP, NJHSP, DHS S&T Critical Infrastructure and Disaster Management, PANYNJ, US Navy, and the US Coast Guard,. These unique interactions and networking opportunities provided a real-world context for the students to frame their research and enhance their summer research experience.

Student survey responses reflected the success of the CSR in meeting its’ objectives. In a true testament to the programs’ impact on student interest, 100% of the student respondents said that the SRI had enhanced their interest in pursuing advanced academic study and/or a career maritime/homeland security domain. In addition, 100% of the students said that they would recommend the SRI to their friends and colleagues.

1.6. Alumni Network and Student Achievements

CSR has actively worked to maintain communication and connections with its SRI alumni. CSR considers these students as ambassadors for the summer research program and more importantly as the next generation of maritime security practitioners and homeland security professionals.

Since the SRI’s first program offering in 2010, CSR’s director of education has continued to stay in touch with the students and continuously provide them with information on relevant academic and professional career opportunities. It is through these continued communications that CSR has been able to track the impacts of the SRI on the continuing activities and career choices of its alumni participants.

Many of the SRI program alumni are now employed in the workforce and continue to credit the SRI as an important factor in their professional development and their ability to successfully obtain employment. Please find below a list of SRI student alumni who are currently engaged in the workforce:

• Angelica Sogor (SRI 2010) recently accepted a position as an Instructional Design Coordinator at the Maritime Institute of Technology and Graduate Studies, in Linthicum Heights, MD. Prior to her position at the Maritime Institute, Angelica completed her Master's Degree in Marine Affairs and Policy at the Rosenstiel School of Marine and Atmospheric Science and authored a Master's Thesis entitled Designing a Centralized Training Academy for Maritime Security. • Hardik Gajjar, Stevens PhD student and SRI 2010 alum, is currently working as a Ports and Intermodal Planner at Parsons Brinkerhoff in Virginia. Gajjar is in the

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process of completing his doctoral work in the area of Economics and Port Resiliency. • Shankar Nilakantan, member of the SRI 2010 HF Radar student research team, is now working as a Radio Frequency Engineer at Qualcomm in San Diego, CA. After completing his Master's Degree in Information Systems at Stevens Institute, • Saiyan Shah (SRI 2010) is working as a Technology Analyst at Nomura Securities in NYC. • Talmor Meir, Stevens Ocean Engineering PhD student worked as a Summer Intern at 4D Security, helping to enhance and operationalize a multi-source geospatial command and control system. 4D Security located in Plainfield, NJ, is a global security firm specializing in security solutions for critical infrastructure, sea ports and national borders.

1.7. Students Mentored and Supported by CSR Academic Partners

In addition to the SRI student support, CSR academic partners have provided mentorship and financial support to 6 students throughout Year 4. These students include high-school research students and PhD level students attending Stevens Institute of Technology. Working in collaboration with CSR faculty, the students have served in various research support capacities and have contributed to the CSR’s research in Acoustics, Emergency Response and Port Resilience. With the support and guidance of the CSR faculty, the students have had the unique opportunity to participate in summer internship programs and have prepared and presented research papers at relevant maritime and homeland security meetings and conferences. Table 5 below provides an overview of the students mentored and supported by the CSR along with their respective accomplishments.

Table 5. Students Mentored and Supported by CSR. CSR Students Type of Accomplishments Academic supported through Support/Advisor Partner DHS/CSR funding Stevens Hardik Gajjar, PhD. Presented paper entitled Institute of Ocean Engineering Research Development of economic Technology Assistantship resiliency assessment framework for marine Advisor: terminals, at the Society Dr. Thomas Wakeman of American Military Engineers' (SAME) Critical Infrastructure Symposium on April 24, 2012, in Arlington,VA.

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Talmor Meir – PhD, Ocean Engineering Research Participated in a 10 week Assistantship field internship at 4-D security in Advisor: Dr. Julie Pullen Monmouth Buryl Fortney, Advisor: Dr. Barbara Presentation to PANYNJ University Undergraduate, T Reagor Workshop on Emergency Software Management Needs on Engineering November 29, 2011, Participation at Stevens’ Justin Schlemm, DHS –CSR Program Senior -Middletown review with presentation South HS Admitted of Floodview and Man to attend Monmouth Overboard. Univ. in Fall 2012.

Nolan Lum, Senior – Freehold HS

Andrew Fishberg Year: Senior – Freehold

1.8. U.S. Coast Guard Auxiliary University Programs – Stevens Institute of Technology on-campus program

Stevens and U.S. Coast Guard Auxiliary University Programs are working together to facilitate an on-campus program for Stevens students. Dr. Hady Salloum, Director of Advanced Research Programs, will serve as the faculty advisor for the Stevens-based USCG Auxiliary University Program and Beth Austin DeFares, CSR Director of Education will serve as the on-campus program coordinator, and will work in direct collaboration with USCG Auxiliary Division 10 and Sector New York to provide students with USCG Auxiliary training and hands-on exercises.

Student members will have the opportunity to conduct aviation exercises, ship boarding and search and rescue operations. The USCG Auxiliary is a volunteer, non-military service unit that assists the USCG in missions related to safety, security, homeland security and marine environmental protection.

1.9. Future Educational Opportunities and Plans

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CSR will continue to grow and extend its educational program offerings with the inclusion of its academic, industry and government partners, together with its many stakeholders and end-users. Plans are being made to develop and enhance the following program areas: • Review and update the Maritime Systems and Maritime Security graduate curriculum. Update the program curriculum according to current trends and needs in the homeland security domain. • New professional development training sessions to be held in collaboration with START. • New professional development courses to be held at Stevens in Washington, DC and at the National Maritime Training Center at the Port of Los Angeles. • CSR will continue to seek opportunities to host DHS Scholar & Fellow interns during the summer of 2013 • Increased collaborations with other DHS CoE’s and stronger networks with MSI and HBCU’s institutions.

2.0 Education Milestones

2012 Summer Research Institute: Outcomes for the SRI 2012 can be found in sections 1.4. – 1.5.4. of the above report. CSR successfully delivered the SRI from June 6 – July 27, 2012. Students completed team research reports and presentations. Student participants engaged in hands-on research projects in collaboration with CSR PI’s. Copies of the students final team presentation can be found on the CSR website at http://www.stevens.edu/csr/education/SRI-2012.html

Professional Development Programs (DC short courses): CSR’s professional development programs are described in section 1.1 of the Y4 report. CSR had scheduled to deliver its Port Security and Sensing Technologies professional development course at the National Maritime Training Center at the Port of Los Angeles., from March 21 – 23, 2012, however, the center did not receive sufficient enrollment to support the delivery of the course. CSR has set forth plans however, to offer the course in the early spring of 2013, at the Stevens Institute of Technology campus location in Washington, DC.

STEM K-12 Education: During the Y4 period CSR began to phase out its STEM K-12 programming, in support of its other program offerings, namely the Summer Research Institute.

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