Multiaspect target identification with wave-based matched pursuits and continuous hidden Markov models

P. Runkle, L. Carin, L. Couchman, T.J. Yoder, J.A. Bucaro

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Multiaspect target identification is effected by fusing the features extracted from multiple scattered waveforms; these waveforms are characteristic of viewing the target from a sequence of distinct orientations. Classification is performed in the maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). We utilize a continuous-HMM paradigm and compare its performance to its discrete counterpart. The feature parsing is performed via wave-based matched pursuits. Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets.
Original languageEnglish (US)
Article number817415
Pages (from-to)1371-1378
Number of pages8
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume21
Issue number12
DOIs
StatePublished - Dec 1 1999
Externally publishedYes

Keywords

  • Hidden Markov models
  • Acoustic scattering
  • Feature extraction
  • Maximum likelihood detection
  • Acoustic measurements
  • Acoustic signal detection
  • Motion measurement
  • Testing
  • Stochastic resonance
  • Target tracking

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