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Detection and estimation of signals by reversible jump Markov chain Monte Carlo computations

  • Petar M. Djuric
  • , Simon J. Godsill
  • , William J. Fitzgerald
  • , Peter J.W. Rayner
  • University of Cambridge

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Markov chain Monte Carlo (MCMC) samplers have been a very powerful methodology for estimating signal parameters. With the introduction of the reversible jump MCMC sampler, which is a Metropolis-Hastings method adapted to general state spaces, the potential of the MCMC methods has risen to a new level. Consequently, the MCMC methods currently play a major role in many research activities. In this paper we propose a reversible jump MCMC sampler based on predictive densities obtained by integrating out unwanted parameters. The proposal densities are approximations of the posterior distributions of the remaining parameters obtained by sampling importance resampling (SIR). We apply the method to the problem of signal detection and parameter estimation of signals. To illustrate the proposed procedure, we present an example of sinusoids embedded in noise.

Original languageEnglish
Title of host publicationProceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
Pages2269-2272
Number of pages4
DOIs
StatePublished - 1998
Event1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 - Seattle, WA, United States
Duration: May 12 1998May 15 1998

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
ISSN (Print)1520-6149

Conference

Conference1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
Country/TerritoryUnited States
CitySeattle, WA
Period05/12/9805/15/98

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