Radar classification using a neural network

Gregory B. Willson

doi:10.1117/12.21170

Abstract

Commonly used signal recognition techniques have many drawbacks. Many signal recognition and analysis techniques rely on complex algorithms which are computationally intensive and require a man in the loop to verify and validate the analysis. Classical signal recognition techniques often are unable to function in near real time. Classical techniques include nearest neighbor classifiers parameter range-matching statistical estimation techniques and heuristic algorithms. Hard-limited decision boundaries can produce ambiguities because signals which are outside these boundaries may not be classified or may be matched to more than one class. Lastly the addition of more signals to the signal recognition database of these algorithms typically necessitates additional software or hardware. We describe the use of an artificial neural network for classifying radar signals collected by a passive receiver. We selected neural classifiers because of their ability to adapt to the environment through training which allows them to avoid many of the problems associated with traditional classifiers. We used an artificial neural network employing a multilayer perceptron with back propagation to solve two common pattern recognition problems encountered when classifying radar signals. The first problem that of pulse sorting or deinterleaving is to sort individual pulses into " bins" associated with the radar emitter each pulse is from. The second problem that of radar classification or identifying radar type is to determine the type (and function) of the radar emitter represented by each bin

Citation Download Citation

Gregory B. Willson, "Radar classification using a neural network", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21170; https://doi.org/10.1117/12.21170

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