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<p><b>Abstract</b>—Multiaspect target identification is effected by fusing the features extracted from multiple scattered waveforms; these waveforms are characteristic of viewing the target from a <it>sequence</it> 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.</p>
Hidden Markov model, matched pursuits, classification.

J. A. Bucaro, T. J. Yoder, L. Carin, P. Runkle and L. Couchman, "Multiaspect Target Identification with Wave-Based Matched Pursuits and Continuous Hidden Markov Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 1371-1378, 1999.
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