Bayesian localization of multiple ocean acoustic sources with environmental uncertainties
AbstractThe article considers simultaneous localization of multiple acoustic sources when properties of the ocean environment. A Bayesian formulation is applied in which the environmental parameters, noise statistics, and locations and complex strengths of multiple sources are considered unknown random variables constrained by acoustic data and prior information. Closed-form maximum-likelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. All sources are successfully localized, with the greatest uncertainty for the weak submerged source for which the marginal density is strongly multi-modal.
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