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Multiaspect Target Identification with Wave-Based Matched Pursuits and Continuous Hidden Markov Models
December 1999 (vol. 21 no. 12)
pp. 1371-1378

Abstract—Multiaspect 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.

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Index Terms:
Hidden Markov model, matched pursuits, classification.
Citation:
Paul Runkle, Lawrence Carin, Luise Couchman, Timothy J. Yoder, Joseph A. Bucaro, "Multiaspect Target Identification with Wave-Based Matched Pursuits and Continuous Hidden Markov Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1371-1378, Dec. 1999, doi:10.1109/34.817415
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