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Bayesian multiple-source localization in an uncertain ocean environment.

Stan E Dosso1, Michael J Wilmut

  • 1School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada. sdosso@uvic.ca

The Journal of the Acoustical Society of America
|June 21, 2011
PubMed
Summary
This summary is machine-generated.

This study develops Bayesian methods for localizing multiple acoustic sources in uncertain ocean environments. The techniques accurately estimate source locations and strengths, even with limited environmental data.

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Area of Science:

  • Ocean acoustics
  • Array signal processing
  • Geophysical inverse theory

Background:

  • Accurate localization of multiple acoustic sources is crucial for underwater surveillance and research.
  • Environmental uncertainties in the ocean (water column, seabed) significantly complicate source localization.
  • Traditional methods struggle with simultaneous localization of multiple sources in poorly characterized acoustic environments.

Purpose of the Study:

  • To develop and evaluate Bayesian methods for simultaneous acoustic source localization under environmental uncertainty.
  • To provide quantitative uncertainty analysis for source parameters and environmental properties.
  • To reduce the complexity and improve the accuracy of acoustic inverse problems.

Main Methods:

  • A Bayesian formulation treating environmental parameters, noise statistics, and source characteristics as unknown random variables.
  • Two estimation approaches: Focalization (maximizing posterior probability density) and Marginalization (integrating posterior probability density using Markov-chain Monte Carlo).
  • Utilizing closed-form maximum-likelihood expressions for source strengths and noise variance to simplify the inversion process.

Main Results:

  • Demonstrated successful application of both Focalization and Marginalization approaches to single- and multi-frequency localization.
  • Achieved accurate estimation of multiple source locations and complex strengths in uncertain shallow-water environments.
  • Provided comprehensive uncertainty quantification for all estimated parameters, aiding inverse problem interpretation.

Conclusions:

  • The developed Bayesian framework offers robust solutions for simultaneous acoustic source localization in challenging, uncertain ocean conditions.
  • Both Focalization and Marginalization methods effectively handle environmental variability and provide reliable source parameter estimates.
  • The quantitative uncertainty analysis is valuable for practical applications and understanding the limitations of acoustic inverse problems.