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16 September 1992 Neural networks applied to radar signal processing: from simulation to hardware
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Signal processing techniques are currently being developed by engineers and analysts to exploit subtle radar phenomena. Research efforts at the Georgia Tech Research Institute, sponsored by the U.S. Air Force, have been directed toward the collection and analysis of data on some of the more subtle characteristics of signals from complex domestic radar systems. Analysis efforts have included both classical and emerging techniques. Applying classical statistical methods of analysis to Georgia Tech's data base on such characteristics, analysts have obtained encouraging results in many areas of radar signal processing. There remain, however, areas involving slowly changing characteristics where an adaptive capability could be critical, particularly in the dense electromagnetic environments that can be anticipated. Recent rapid advances in neural network techniques, when accompanied by advances in parallel processing hardware that will be needed to implement the networks for future field use, have resulted in the potential to provide this adaptability to changing parametric conditions. To assess that potential, the U.S. Air Force initiated two separate programs at the Georgia Tech Research Institute. Results from one program include an evaluation of a number of neural network paradigms for their appropriateness for processing radar waveforms to extract subtle information contained there. Test results using selected neural networks with various forms of collected data are presented. Results from another ongoing effort include a discussion of an effort to implement in hardware one of the neural networks tested.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Katharine L. Schlag, M. T. Kopp, A. N. Nunemaker, J. T. Spillane, Katherine C. Petty, and R. A. Thompson "Neural networks applied to radar signal processing: from simulation to hardware", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992);


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