Active impulsive echo discrimination in shallow water by mapping target physics-derived features to classifiers

One of the most difficult challenges in shallow-water active sonar processing is false-alarm rate reduction via active classification. In impulsive-echo-range processing, an additional challenge is dealing with stochastic impulsive source variability. The goal of active classification is to remove as much clutter as possible while maintaining an acceptable detection performance. Clutter in this context refers to any non-target, threshold-crossing cluster event. In this paper, we present a clutter-reduction algorithm using an integrated pattern-recognition paradigm that spans a wide spectrum of signal and image processing-target physics, exploration of projection spaces, feature optimization, and mapping the decision architecture to the underlying good-feature distribution. This approach is analogous to a classify-before-detect strategy that utilizes multiple informations to arrive at the detection decision. After a thorough algorithm evaluation with real active sonar data, we achieved over an order of magnitude performance improvement in clutter reduction with our methodology over that of the baseline processing.