Development of Algorithms for Characterizing Interleaved Emitter Pulse Trains with Complex Modulations Navy SBIR FY2013.1 Sol No.: Navy SBIR FY2013.1 Topic No.: N131-052 Topic Title: Development of Algorithms for Characterizing Interleaved Emitter Pulse Trains with Complex Modulations Proposal No.: N131-052-0543 Firm: Research Associates of Syracuse 111 Dart Circle Rome, New York 13441 Contact: Dennis Stadelman Phone: (315) 339-4800 Web Site: www.ras.com Abstract: This SBIR develops clustering and de-interleaving algorithms to process non-contiguous clusters of pulses, collected from acquisition scanning receivers periodically sampling (spectrally, spatially, and temporally) subsets of the signal environment, to better perform Emitter Identification (EID). The end result will be improved correct EID, with confidence level, and a corresponding reduction in the size of candidate emitter lists and ambiguities. It addresses a wide variety of challenging emitter signal classes for which current approaches are noted to have problems (RF agility, pulse repetition frequency (PRF) agility, pulse-width agility (PW), combinations of RF/PRF and/or PW agilities, and complex modulations). It also addresses multiple interleaved PRFs of different pulse-widths and modulations generated from single emitters. The approach leverages and refines existing RAS algorithms and software modules (C/C++ and MATLAB) such as clustering using multiple parameters measured on a single pulse, time based de-interleaving, RF Agile clustering, and cluster correlation grouping clusters collected in non-contiguous time intervals. Approaches are assessed and selected for PHASE II software development and demonstration within the SEWIP Block II architecture. During and after Phase II, RAS will work with SEWIP Block II prime Lockheed Martin and the government to define, install and demonstrate the technology on a suitable test-bed. Benefits: The key benefit this SBIR provides to the government is a greatly enhanced capability to cluster and de-interleave emitters employing multi-dimensional agility (i.e. RF and/or PRF, and/or PW), ever increasing complex intentional modulations, and those emitters operating over very wide RF bandwidths and intermittent collections of data as obtained with a scanning wideband acquisition and set-on hybrid receiver. The effort will enable more accurate emitter identification with less ambiguities and improved situational awareness. The ES system benefits obtained will provide the fleet with improved automatic timely reporting and information dissemination of surface activity to support ensuring safe passage, maintaining tactical superiority and asserting control in underwater operations. The primary military application to be initially addressed is the NAVSEA Surface EW Improvement Program (SEWIP) Block II. Several other NAVY candidate applications have been identified. One leading example is the PMS-435 AN/BLQ-10 (V) EW Modernization for both Virginia and Ohio class submarines (Next Generation ES system). Algorithms from this SBIR would have application in future Block IV and V upgrades and would be integrated with those developed on a related SBIR topic for Enhanced De-interleavers (N122-133). RAS will explore, with the COTR, applications to these programs as well as others recommended by the government to transition Phase II technology into a Phase III The clustering / de-interleaving algorithm concepts developed have numerous military and commercial applications. They can be employed in a wide variety of ES, ELINT or SIGINT applications and missions where scanning receiver architectures are used to rapidly intercept, detect, cluster / de-interleave and characterize multiple, same-type, complex signals. Potential applications in the private sector include wireless waveform characterization and sustaining operation in the presence of fading and /or multipath, fidelity assessment and classification, passive tracking of RF devices such as cell phones, and RF Identification verification. These will be explored in more detail in Phase I and Phase II. Key benefits of the proposed RAS approach are: 1) Multi-pronged approach addresses a variety of challenging radar classes 2) Adaptable, modular and reconfigurable software a. New or refined clustering / deinterleaving algorithms can be readily inserted 3) Automatic processing reduces operator workload 4) Leverages RAS proven expertise and existing MATLAB and C/C++ code, algorithms and end-to-end EW processing test bed; real-world stressing/challenging signal experience and data, complex IMOP characterization and typing algorithms, waveform detection and characterization. 5) Key Performance Parameters, suitable for assessing cluster/deinterleaver operation are use to establish quantitative performance. 6) Confidence levels are provided to improve ambiguity assessment and reduction Return