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Proceedings Paper

Multiaspect acoustic identification of submerged elastic targets via wave-based matching pursuits and continuous hidden Markov models
Author(s): Paul R. Runkle; Lawrence Carin; Luise S. Couchman; Joseph A. Bucaro; Timothy J. Yoder
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Paper 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.

Paper Details

Date Published: 24 August 1999
PDF: 9 pages
Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); doi: 10.1117/12.359982
Show Author Affiliations
Paul R. Runkle, Duke Univ. (United States)
Lawrence Carin, Duke Univ. (United States)
Luise S. Couchman, Naval Research Lab. (United States)
Joseph A. Bucaro, Naval Research Lab. (United States)
Timothy J. Yoder, SFA, Inc. (United States)

Published in SPIE Proceedings Vol. 3718:
Automatic Target Recognition IX
Firooz A. Sadjadi, Editor(s)

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