A what-and-where fusion neural network for recognition and tracking of multiple radar emitters

Neural Networks : the Official Journal of the International Neural Network Society
E GrangerP Lavoie

Abstract

A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is considered 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 ARTMAP compares favorably to several other approaches when performance is measured in terms of accuracy and computational complexity. Incorporat...Continue Reading

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Citations

Nov 20, 2015·Australasian Physical & Engineering Sciences in Medicine·H Jahani FarimanPaul H Chappell
Aug 6, 2010·Neural Networks : the Official Journal of the International Neural Network Society·Jukka-Pekka KauppiUlla Ruotsalainen

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