24 August 1999 Multiaspect acoustic identification of submerged elastic targets via wave-based matching pursuits and continuous hidden Markov models
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Abstract
A wave-based matching-pursuits algorithm is used to parse multi-aspect time-domain backscattering data into its underlying wavefront-resonance constituents, or features. Consequently, the N multi-aspect waveforms under test are mapped into N feature vectors, yn. Target identification is effected by fusing these N vectors in a maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). In this paper, we utilize a continuous-HMM paradigm, and compare its performance to its discrete counterpart. Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets.
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Paul R. Runkle, Lawrence Carin, Luise S. Couchman, Joseph A. Bucaro, Timothy J. Yoder, "Multiaspect acoustic identification of submerged elastic targets via wave-based matching pursuits and continuous hidden Markov models", Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); doi: 10.1117/12.359982; https://doi.org/10.1117/12.359982
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