Efficient bayesian multi-source localization


  • Stan E. Dosso School of Earth and Ocean Sciences, University of Victoria, Victoria, BC V8W 3P6, Canada
  • Michael J. Wilmut School of Earth and Ocean Sciences, University of Victoria, Victoria, BC V8W 3P6, Canada


Acoustics, Iterative methods, Simulated annealing, Spurious signal noise, Acoustic data, Acoustic sources, Bayesian formulation, Bayesian information criterion, Localization algorithm, Multisources, Noise variance, Ocean acoustics, Prior information, Source parameters, Source strength


An efficient approach to the simultaneous localization of an unknown number of ocean acoustic sources, based on minimizing the Bayesian information criterion (BIC) over source parameters, is illustrated. A Bayesian formulation is developed in which the number, locations, and complex strengths of an unknown number of sources are considered random variables constrained by acoustic data and prior information. Implicit sampling over source strengths and noise variances is also derived. The multiple-source localization algorithm developed optimizes over the number and locations of acoustic sources, and complex sources strengths and noise variance at each frequency, by minimizing the BIC. It is found that BIC drops quickly and the number of sources settles into the correct value of 5 by about iteration 30 of the simulated annealing process.




How to Cite

Dosso SE, Wilmut MJ. Efficient bayesian multi-source localization. Canadian Acoustics [Internet]. 2012 Sep. 1 [cited 2022 Aug. 8];40(3):72-3. Available from: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/2545



Proceedings of the Acoustics Week in Canada

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