Multi-aspect target identification with wave-based matching pursuits and continuous hidden Markov models

P. Runkle, L. Carin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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, y/sub 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). The algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets.
Original languageEnglish (US)
Title of host publication1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)
PublisherIEEE
Pages2115-2118
Number of pages4
Volume4
ISBN (Print)0-7803-5041-3
DOIs
StatePublished - Mar 19 1999
Externally publishedYes
Event1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) - Phoenix, AZ, USA
Duration: Mar 15 1999Mar 19 1999

Conference

Conference1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)
Period03/15/9903/19/99

Keywords

  • Matching pursuit algorithms
  • Hidden Markov models
  • Acoustic scattering
  • Electromagnetic scattering
  • Scattering parameters
  • Physics
  • Dictionaries
  • Pursuit algorithms
  • Maximum likelihood detection
  • Acoustic pulses

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