Bayesian tracking of multiple acoustic sources in an uncertain ocean environment

Abstract

This letter develops a Bayesian approach to matched-field tracking of multiple acoustic sources in a poorly-known environment. Markov-chain Monte Carlo methods explicitly sample the posterior probability density over source locations and environmental parameters, while analytic maximum-likelihood solutions for complex source strengths and noise variance in terms of the explicit parameters allow these parameters to be sampled efficiently. This produces a time-ordered sequence of joint marginal probability distributions over source range and depth, from which optimal track estimates and uncertainties are extracted. Synthetic examples consider tracking a submerged source in the presence of a louder shallow interferer in an unknown environment.

Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean

“…Zurk et al presented three motion mitigation techniques to improve the localization performance of moving targets [4]. The second type employs methods based on the Bayesian approach, utilizing the optimization of source parameters with the Markov Chain Monte Carlo sampling method [7,8,9,10,11]. Dosso and Wilmut provided two approaches utilizing the posterior probability density over the parameters of source position in uncertain shallow water [7].…”