Persistent Maritime Target Tracking Using Automated Target Fingerprinting and Discrimination Navy SBIR FY2014.1 Sol No.: Navy SBIR FY2014.1 Topic No.: N141-016 Topic Title: Persistent Maritime Target Tracking Using Automated Target Fingerprinting and Discrimination Proposal No.: N141-016-0820 Firm: Systems & Technology Research 600 West Cummings Park Suite 6500 Woburn, Massachusetts 01801-7238 Contact: Mark McClure Phone: (703) 493-0057 Web Site: www.STResearch.com Abstract: Systems & Technology Research (STR), together with our sub-contractor Leidos Inc., proposes to develop an advanced feature aided tracking and discrimination (FAT-D) system for persistent maritime tracking in dense littoral environments. The proposed FAT-D system consists of novel representations of ship RCS features and wake signatures, and efficient algorithms that match features extracted from HRR profiles and ISAR images to ships. During Phase I base period, we will enhance in-house MATLAB-based maritime simulation environment for FAT-D system testing with parametric models of large amplitude motion, hard body and wake signatures, and simulated HRR and ISAR data provided by Leidos for multiple vessels of different classes under varying sea states. We will also evaluate the system using measured data, in particular data from the ZPY-4 flight tests planned for 2014 June. At the conclusion of Phase I, we will have completed a thorough testing of the proposed FAT-D algorithms using simulated and measured data, and integrated the algorithms into the OSI test bed. This will provide the foundation for further development and real-time implementation on-board the Navy Fire Scout, and flight testing in Phase II. Benefits: If the proposed development is successful, we will have developed a feature aided tracking and discrimination system for the US Navy maritime applications. Such a system will significantly improve situational awareness of the surface picture by enabling surveillance of a larger region of interest, more efficient target interrogation, extended track life, improved target classification performance, and optimization of radar resources. Return