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

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

Research output: Contribution to journalArticlepeer-review

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-HM 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.
Original languageEnglish (US)
Pages (from-to)459-467
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3718
StatePublished - Jan 1 1999
Externally publishedYes

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