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This study introduces a robust Bayesian method for acoustic source localization in uncertain ocean environments. The approach improves accuracy by accounting for environmental mismatch, enhancing reliable underwater acoustic detection.

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Area of Science:

  • Ocean acoustics
  • Signal processing
  • Geophysics

Background:

  • Matched-field acoustic source localization is sensitive to environmental uncertainties.
  • Environmental mismatch significantly degrades localization performance.

Purpose of the Study:

  • Develop a Bayesian approach to enhance robustness against environmental mismatch in acoustic source localization.
  • Quantify localization uncertainty by integrating over environmental variability.

Main Methods:

  • Model the waveguide Green's function as an uncertain random vector.
  • Employ Bayesian inference to obtain a joint marginal probability distribution for source range and depth.
  • Approximate high-dimensional integration using a 1D integration over a correlation measure and modal analysis for covariance matrix approximation.

Main Results:

  • The proposed method provides efficient and reliable source localization.
  • Demonstrated improved robustness to environmental mismatch compared to other methods.
  • Successfully quantified localization uncertainties.

Conclusions:

  • The developed Bayesian approach offers a robust solution for acoustic source localization in uncertain oceanic environments.
  • The method effectively mitigates performance degradation caused by environmental mismatch.
  • Accurate localization and uncertainty quantification are achieved.