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Reverberation often limits the performance of active sonar systems. In particular, backscatter off of a rough ocean floor can obscure target returns and/or large bottom scatterers can be easily confused with water column targets of interest. Conventional active sonar detection involves constant false alarm rate (CFAR) normalization of the reverberation return which does not account for the frequency-selective fading caused by multipath propagation. This paper presents an alternative to conventional reverberation estimation motivated by striations observed in time-frequency analysis of active sonar data. A mathematical model for these reverberation striations is derived using waveguide invariant theory. This model is then used to motivate waveguide invariant reverberation estimation which involves averaging the time-frequency spectrum along these striations. An evaluation of this reverberation estimate using real Mediterranean data is given and its use in a generalized likelihood ratio test based CFAR detector is demonstrated. CFAR detection using waveguide invariant reverberation estimates is shown to outperform conventional cell-averaged and frequency-invariant CFAR detection methods in shallow water environments producing strong reverberation returns which exhibit the described striations.

Spatially distributed arrays of permanently attached ultrasonic sensors are being considered for structural health monitoring systems. Most algorithms for analyzing the received signals are based upon change detection whereby baselines from the undamaged structure are subtracted from current signals of interest, and the residual signals are analyzed. In particular, delayand-sum algorithms applied to the residual signals have been shown to be effective for imaging damage in plate-like structures that support propagation of guided waves. Here we consider minimum variance processing of the residual signals, which is an adaptive beamforming method in common use for processing of radar signals where the weights are adjusted at each pixel location prior to summation based upon actual and expected signal amplitudes. Experimental results from a sparse sensor array show that this processing method can provide a significantly improved signal-tonoise ratio by suppressing unwanted sidelobes in the image.

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