Wavelet-domain hidden Markov models for signal detection and classification

M. S. Crouse, R. D. Nowak, Kerim Mhirsi, Richb G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


This paper addresses the problem of detection and classification of complicated signals in noise. Classical detection methods such as energy detectors and linear discriminant analysis do not perform well in many situations of practical interest. We introduce a new approach based on hidden Markov modeling in the wavelet domain. Using training data, we fit a hidden Markov model (HMM) to the wavelet transform to concisely represent its probabilistic time-frequency structure. The HMM provides a natural framework for performing likelihood ratio tests used in signal detection and classification. We compare our approach with classical methods for classification of nonlinear processes, change-point detection, and detection with unknown delay.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsF.T. Luk
Number of pages12
StatePublished - 1997
EventAdvanced Signal Processing: Algorithms, Architectures and Implementations VII - San Diego, CA, United States
Duration: Jul 28 1997Jul 30 1997


OtherAdvanced Signal Processing: Algorithms, Architectures and Implementations VII
Country/TerritoryUnited States
CitySan Diego, CA


  • Classification
  • Detection
  • Hidden Markov models
  • Wavelets

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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