Passive sonar processing using neural networks

Abstract

The utilization of a two-stage neural network architecture for the detection of targets in a passive, listen-only sonar is discussed. The two-stage network consists of a first-stage Hopfield network to suppress noise, and a second stage using a bidirectional associative memory (BAM) to make the decision as to whether a target has been detected or not. A second architecture using only a single BAM stage is also presented for illustrative purposes. The target is assumed to be emitting a single tone sinusoid as its signature. The system also assumes only white Gaussian noise perturbation to the signal. It is shown that this network structure provides correct detection at a signal-to-noise ratio of -21 dB, a 6 dB improvement in target detection over a similar network using a perceptron in the second stage. Performance is shown to be limited to the size of the Hopfield network, in the first stage, and to the training set applied to it.<>