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Radar signal categorization using a neural networkNeural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.
Document ID
19910012469
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Anderson, James A. (Brown Univ. Providence, RI, United States)
Gately, Michael T. (Brown Univ. Providence, RI, United States)
Penz, P. Andrew (Brown Univ. Providence, RI, United States)
Collins, Dean R. (Brown Univ. Providence, RI, United States)
Date Acquired
September 6, 2013
Publication Date
February 1, 1991
Publication Information
Publication: NASA, Lyndon B. Johnson Space Center, Proceedings of the 2nd Joint Technology Workshop on Neural Networks and Fuzzy Logic, Volume 1
Subject Category
Cybernetics
Accession Number
91N21782
Funding Number(s)
CONTRACT_GRANT: F33615-87-C-1454
CONTRACT_GRANT: N00014-86-K-0600
CONTRACT_GRANT: NSF BNS-85-18675
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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