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Development of Algorithms for Characterizing Interleaved Emitter Pulse Trains with Complex Modulations


OBJECTIVE: The objective is to develop a scanning receiver capability that infers or interpolates missing pulse data from noncontiguous pulse clusters, regardless of modulation complexity, to improve Navy Electronic Warfare (EW) systems. DESCRIPTION: The process of identifying Radio Frequency emitters, such as surface ship radars and missile seekers, for current Navy EW systems is very dependent on the separation or de-interleaving of overlapping pulse train signals. Current as well as new proposed techniques require that the pulsed data be collected in contiguous sets or blocks. This requires the receiver to be tuned to the emitter frequency for extended periods of time (Ref 1). A channelized receiver could be used but their cost makes this prohibitive for the frequency coverage required. Scanning receivers are therefore used. A major problem with these is that they can only sample the desired signal for a relatively short time before they must be tuned to another frequency; if they dwell too long at a particular frequency, probability of intercept will increase. The Navy has a need for a low cost technology that identifies RF emitters utilizing scanning receivers that obtain the same performance levels as wideband receivers. The new technology needs to infer or interpolate the data that is not detected when the scanning receiver is tuned away from the signal of interest, causing missing data (Ref 2, 6). An intelligent system, such as a neural net or genetic algorithm, if trained properly, may be able to infer or interpolate the missing data since even the most complex modulations show, even if only statistically, some unique characteristics that current processing methods do not detect. Genetic and Neural (Ref 3, 6, 7) refer to the application of biological principles to programming schemes and methods. The application of these processes to this topic has not yet been developed, only speculated upon (Ref 3, 4, 5). This technology will address the Navy need of reducing operating and maintenance costs. PHASE I: The company will develop concepts for an improved scanning receiver that will develop processing techniques to infer or interpolate, missing pulse data from noncontiguous pulse clusters regardless of modulation complexity and that meets the requirements described above. The company will demonstrate the feasibility of the concepts in meeting Navy needs and will establish that the concepts can be feasibly developed into a useful product for the Navy. Feasibility will be established by testing and analytical modeling. The small business will provide a Phase II development plan with performance goals and key technical milestones, and will address technical risk reduction. PHASE II: Based on the results of Phase I and the Phase II development plan, the small business will develop a prototype for evaluation as appropriate. System performance will be demonstrated through prototype evaluation and modeling or analytical methods over the required range of parameters. Evaluation results will be used to refine the prototype into an initial design that will meet Navy requirements. The company will prepare a Phase III development plan to transition the technology to Navy use. PHASE III: If Phase II is successful, the company will be expected to support the Navy in transitioning the technology for Navy use. The company will develop an improved scanning receiver for evaluation to determine its effectiveness in an operationally relevant environment. The company will support the Navy for test and validation to certify and qualify the system for Navy use. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Radar and communications systems can also use this technology to enhance their performance and economize on hardware required. Immunity to fading and other signal disruptions would also be enhanced. Cell phone, airport, weather, and automobile industries would benefit from this technology. REFERENCES: 1) Wiley, Richard G."ELINT: The Interception and Analysis of Radar Signals"Artech House, Norwood, MA, 2006. 2) Haykin, S. and Thomson, D."Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem."Proc. IEEE, 1998. 86(11), 23252344. 3) Nikias, C. L. and Petropulu, A. P. Higher-Order Spectral Analysis: A Nonlinear Signal Processing Framework. Englewood Cliffs, NJ, USA: Prentice Hall, 1993. 4) Nikias, C. L. and Shao, M. Signal Processing with Alpha-Stable Distributions and Applications. New York, NY, USA: Wiley-Interscience, 1995. 5) Cichocki, A. and Amari, S. Adaptive Blind Signal and Imaging Processing: Learning Algorithms and Applications. New York, NY, USA: Wiley, 2002. 6) Black, D. C., Sciortino Jr., J. C., and Altoft, J. R."High-accuracy, Low-ambiguity Emitter Classification Using an Advanced Dempster-Shafer Algorithm."PROCEEDINGS-SPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING. 2004. 7)
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