Acoustic localization of an unknown number of sources in an uncertain ocean environment

Stan E. Dosso

Abstract


This paper develops a new approach to simultaneous localization of an unknown number of ocean acoustic sources when properties of the environment are poorly known, based on minimizing the Bayesian information criterion (BIC) over source and environmental parameters. A Bayesian formulation is developed in which water-column and seabed parameters, noise statistics, and the number, locations, and complex strengths (amplitudes and phases) of multiple sources are considered unknown random variables constrained by acoustic data and prior information. The BIC, which balances data misfit with a penalty for extraneous parameters, is minimized using hybrid optimization (adaptive simplex simulated annealing) over environmental parameters and Gibbs sampling over source locations. Closed- form maximum-likelihood expressions for source strength and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality of the inversion. Gibbs sampling and the implicit formulation provide an efficient scheme for adding and deleting sources during the optimization. A simulated example is presented which considers localizing a quiet submerged source in the presence of two loud interfering sources in a poorly-known shallow-water environment.

Keywords


Acoustic noise; Acoustics; Simulated annealing; Acoustic data; Acoustic localization; Bayesian formulation; Bayesian information criterion; Environmental parameter; Gibbs sampling; Hybrid optimization; Implicit formulation; Interfering sources; Multiple source; Noise statistics; Noise variance; Ocean acoustics; Ocean environment; Prior information; Shallow-water; Source location; Source strength

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