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dc.contributor.authorGranger, Ericen_US
dc.contributor.authorRubin, Marken_US
dc.contributor.authorGrossberg, Stephenen_US
dc.contributor.authorLavoie, Pierreen_US
dc.date.accessioned2011-11-14T19:02:49Z
dc.date.available2011-11-14T19:02:49Z
dc.date.issued2000-09en_US
dc.identifier.urihttp://hdl.handle.net/2144/2276
dc.description.abstractA neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is combined with position-specific information from active emitters in a scene. Type-specific parameters of the input pulse stream are fed to a neural network classifier trained on samples of data collected in the field. Meanwhile, a clustering algorithm is used to separate pulses from different emitters according to position-specific parameters of the input pulse stream. Classifier responses corresponding to different emitters are separated into tracks, or trajectories, one per active emitter, allowing for more accurate identification of radar types based on multiple views of emitter data along each emitter trajectory. Such a What-and-Where fusion strategy is motivated by a similar subdivision of labor in the brain. The fuzzy ARTMAP neural network is used to classify streams of pulses according to radar type using their functional parameters. Simulation results obtained with a radar pulse data set indicate that fuzzy AIUMAP compares favorably to several other approaches when performance is measured in terms of accuracy and computational complexity. Incorporation into fuzzy ARTMAP of negative match tracking (from ARTMAP-IC) facilitated convergence during training with this data set. Other modifications improved classification of data that include missing input pattern components and missing training classes. Fuzzy ARTMAP was combined with a bank of Kalman filters to group pulses transmitted from different emitters based on their position-specific parameters, and with a module to accumulate evidence from fuzzy ARTMAP responses corresponding to the track defined for each emitter. Simulation results demonstrate that the system provides a high level of performance on complex, incomplete and overlapping radar data.en_US
dc.description.sponsorshipDefense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409) (S. G. and M. A. R.); National Science Foundation (IRI-97-20333) (S. G.); Natural Sciences and Engineering Research Council of Canada (E. G.); Office of Naval Research (N00014-95-l-0657) (S.G.).en_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-2000-029en_US
dc.rightsCopyright 2000 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectRadar
dc.subjectElectronic support measures
dc.subjectPattern recognition
dc.subjectData fusion
dc.subjectNeural networks
dc.subjectARTMAP
dc.subjectKalman filter
dc.titleA What-and-Where Fusion Neural Network for Recognition and Tracking of Multiple Radar Emittersen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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