Cascaded Neural-Analog Networks for Real Time Decomposition of Superposed Radar Signals in the Presence of Noise.

Abstract

Among the numerous problems which arise in the context of radar signal processing is the problem of extraction of information from a noise corrupted signal. In this application the signal is assumed to be the superposition of outputs from multiple radar emitters. Associated with the output of each emitter is a unique set of parameters which are in general unknown. Significant parameters associated with each emitter are (i) the pulse repetition frequencies, (ii) the pulse durations (widths) associated with pulse trains and (iii) the pulse amplitudes: A superposition of the outputs of multiple emitters together with additive noise is observed at the receiver. In this study we consider the problem of decomposing such a noise corrupted linear combination of emitter outputs into an underlying set of basis signals while also identifying the parameters associated with each of the emitters involved. Foremost among our objectives is to design a system capable of performing this decomposition/classification in a demanding realtime environment. We present here a system composed of three cascaded neural-analog networks which, in simulation, has demonstrated an ability to nominally perform the task of decomposition and classification of superposed radar signals under extremely high noise conditions.