title: 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.
authors.affiliation: School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada. sdosso@uvic.ca
authors.forename: Stan E
authors.initials: SE
authors.lastname: Dosso
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Wilmut
mesh_terms: D000162:Acoustics; D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009038:Motion; D009622:Noise; D009792:Oceans and Seas; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013018:Sound Spectrography; D013997:Time Factors; D014867:Water
chemical_list: D014867:Water