Neural detectors for signals in non-Gaussian noise

Authors:
Viswanath Ramamurti;Sathyanarayan S. Rao;Prashant P. Gandhi
Affiliations:
Department of Electrical and Computer Engineering, Villanova University, Villanova, PA;Department of Electrical and Computer Engineering, Villanova University, Villanova, PA;Department of Electrical and Computer Engineering, Villanova University, Villanova, PA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

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Abstract

In this paper, we train a neural network for the purpose of detecting a known signal corrupted by additive Gaussian as well as non-Gaussian noise of impulsive type. In the presence of Gaussian noise, we show that performance of a properly trained neural network is very similar to that of the optimum matched filter detector. In the presence of non-Gaussian noise, however, neural detectors are shown to perform better than both matched filter and locally optimum detectors.