Multiaspect acoustic identification of submerged elastic targets via wave-based matching pursuits and continuous hidden Markov models

Paul R. Runkle; Lawrence Carin; Luise S. Couchman; Joseph A. Bucaro; Timothy J. Yoder

doi:10.1117/12.359982

24 August 1999 Multiaspect acoustic identification of submerged elastic targets via wave-based matching pursuits and continuous hidden Markov models

Paul R. Runkle, Lawrence Carin, Luise S. Couchman, Joseph A. Bucaro, Timothy J. Yoder

Author Affiliations +

Proceedings Volume 3718, Automatic Target Recognition IX; (1999) https://doi.org/10.1117/12.359982
Event: AeroSense '99, 1999, Orlando, FL, United States

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, y_n. 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.

Citation Download Citation

Paul R. Runkle, Lawrence Carin, Luise S. Couchman, Joseph A. Bucaro, and 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); https://doi.org/10.1117/12.359982

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