IER clutter reduction in shallow water

One of the most difficult challenges in shallow water active sonar processing is false alarm rate reduction via active classification. The goal of active classification is to remove as much clutter as possible while maintaining an acceptable P_d performance. Clutter in this context refers to any nontarget, threshold crossing cluster events. We present a novel approach to clutter reduction in shallow water using an integrated pattern recognition paradigm. With our understanding of target physics, we identify clues or features useful for clutter rejection by projecting raw data onto appropriate transformation spaces to achieve both energy compaction and subspace filtering. We explore various time-frequency distribution (TFD) functions, compressed phase map, and speech related processing domains to extract clues that provide insights into local multipath patterns and detailed signal dynamics. We perform thorough feature fusion and optimization, and evaluate clutter reduction performance in terms of receiver operating characteristic (ROC) curves. Our real data analyses indicate that we can achieve over an order of magnitude performance improvement in clutter reduction over that of the baseline processing