Wavelet-based statistical signal processing using hidden Markov models

Matthew S. Crouse, Robert D. Nowak, Richard G. Baraniuk

Research output: Contribution to journalArticlepeer-review

1355 Scopus citations

Abstract

Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMM's) that concisely models the statistical dependencies and non-Gaussian statistics encountered in real-world signals. Wavelet-domain HMM's are designed with the intrinsic properties of the wavelet transform in mind and provide powerful, yet tractable, probabilistic signal models. Efficient expectation maximization algorithms are developed for fitting the HMM's to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMM's, we develop novel algorithms for signal denoising, classification, and detection. l

Original languageEnglish (US)
Pages (from-to)886-902
Number of pages17
JournalIEEE Transactions on Signal Processing
Volume46
Issue number4
DOIs
StatePublished - 1998

Keywords

  • Hidden markov model
  • Probabilistic graph
  • Wavelets

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

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