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

Abstract

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.

DOI: 10.1109/34.817415

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@article{Runkle1999MultiaspectTI, title={Multiaspect Target Identification with Wave-Based Matched Pursuits and Continuous Hidden Markov Models}, author={Paul Runkle and Lawrence Carin and Luise Couchman and Timothy J. Yoder and Joseph A. Bucaro}, journal={IEEE Trans. Pattern Anal. Mach. Intell.}, year={1999}, volume={21}, pages={1371-1378} }