N141-016 TITLE: Persistent Maritime Target Tracking Using Automated Target Fingerprinting and Discrimination

TECHNOLOGY AREAS: Air Platform, Sensors

ACQUISITION PROGRAM: PMA 299

RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports): This topic is "ITAR Restricted". The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130, which control the export of defense-related material and services, including the export of sensitive technical data. Foreign Citizens may perform work under an award resulting from this topic only if they hold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.

OBJECTIVE: Develop a Feature Aided Tracking and Discrimination (FAT-D) approach operating under the control of an automated resource manager to provide persistent target tracking (across extended breaks and even over multiple missions) of vessels (including ships and small boats) along with non-geometric discrimination capabilities to separate small boats, semi-submersibles and periscope masts from potential confusing returns generated by sea spikes and flotsam.

DESCRIPTION: Improvements in surface target classification performance, track lifetime, and track association in dense target environments and clutter backgrounds are needed. Utilization of a Feature Aided Tracking and Discrimination (FAT-D) capability could significantly improve situational awareness of the surface picture through surveillance of a larger region of interest, more efficient target interrogation, extended track life, improved classification/identification performance, and optimization of radar resources. FAT-D approaches are not currently utilized in standard Navy airborne radar systems. When compared to kinematic-only trackers the FAT-D approach should provide 100x track life improvement as quantified by initial track identity lifetime (ITIL) and dominant track identify lifetime (DTIL) metrics. The general approach follows a chain of increasing confidence in target classification using traditional surface search modes to first detect moving vessels and to classify them based on their estimated length information and high range resolution bulk filtering to identify those vessels of interest. The FAT-D approach should exploit vessel length information during a search and acquisition mode to identify different classes of vessels. By interleaving the high range resolution mode with search modes a more efficient use of radar resources is possible along with the ability to pre-filter targets before committing the radar to longer Inverse Synthetic Aperture Radar (ISAR) dwells. Extraction of target discrimination features is performed simultaneously to separate target types though the exploitation of non-geometric features including the nature of the object�s wake transfer impedance. The desired deliverable is a FAT-D software application suitable for demonstration with a candidate Navy radar system.

PHASE I: Develop, and demonstrate feasibility of FAT-D algorithms using realistic simulated data in a lab environment representative of a candidate Navy radar system. Demonstrate the improvement in target association performance across extended track breaks in stressing high-density littoral environments.

PHASE II: Implement the algorithms developed in Phase I in a real-time environment and demonstrate with the candidate radar in a field test. Demonstrate how the FAT-D application can be integrated with a candidate Navy radar system.

PHASE III: Transition the developed technology to appropriate platforms/sensors.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: The most direct dual use applications are in remote economic exclusion zone (EEZ) monitoring or possibly search and rescue operations.

REFERENCES:
1. Feng, Zhao, Hong-zhong, Zhao, Meng-jun, Huang, & Wei, Qui. (2010). Using target radial length for data association in multiple-target tracking, Signal Processing (ICSP), 2010 IEEE 10th International Conference on, Page(s): 2257 � 2260

2. Marti, E.D., Garcia, J., & Crassidis, J.L. (2012). Improving multiple-model context-aided tracking through an autocorrelation approach. Information Fusion (FUSION), 2012 15th International Conference on, Page(s): 1822 - 1829

KEYWORDS: Maritime Surveillance; Radar; Resource Management; feature aided tracking; detection and discrimination; small boats

** TOPIC AUTHOR (TPOC) **

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