Exploiting differences in underwater acoustic signal and noise distributions to improve signal detection in low signal-to-noise ratio

Abstract

Traditional models for acoustic signals and noise in underwater detection utilize assumptions about the underlying distributions of these quantities to make algorithms more analytically and computationally tractable. Easily estimated properties of the signal, like the mean amplitude or power, are then calculated and used to form predictions about the presence or absence of these signals. While appropriate for high SNR, quantities like the mean amplitude may not give reliable detection for SNR at or below 0 dB. Fluctuation based processors, utilizing additional statistics of received pressure, offer an alternative form of detection when features of the received signal beyond changes in mean amplitude are appreciably altered by the presence of a signal. An overview of fluctuation based processing will be given, with a focus on the underlying statistical phenomena that grant this method efficacy. Work sponsored by the Office of Naval Research in Undersea Signal Processing.