Multiaspect Target Identification with Wave-Based Matched Pursuits and Continuous Hidden Markov Models

ÐMultiaspect target identification is effected by fusing the features extracted from multiple scattered waveforms; these waveforms are characteristic of viewing the target from a sequence of distinct orientations. Classification is performed in the maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). We utilize a continuous-HMM paradigm and compare its performance to its discrete counterpart. The feature parsing is performed via wave-based matched pursuits. Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets. Index TermsÐHidden Markov model, matched pursuits, classification.