Bayesian ocean acoustic source track with environmental uncertainty
Keywords:Acoustic noise measurement, Monte Carlo methods, Oceanography, Probability density function, Acoustical properties, Bayesian, Environmental parameter, Environmental uncertainty, Gibbs sampling, Information contents, Marginal probability, Markov chain Monte Carlo, Metropolis-Hastings samplings, Moving acoustic sources, Ocean acoustics, Posterior probability, Source location, Tracking process, Uncertainty distributions
AbstractA study was conducted to explore matched-field tracking of a moving acoustic source in the ocean when acoustical properties of the environment are poorly known. The goal was to determine track uncertainty distributions, quantifying the information content of the tracking process. Source information was extracted from the posterior probability density (PPD) by integrating over unknown environmental parameters. Source information was extracted to obtain a time-ordered series of joint marginal probability surfaces over source range and depth. Marginal PPDs were computed numerically using efficient Markov-chain Monte Carlo (MCMC) methods, including Metropolis-Hastings sampling over environmental parameters and heat-bath Gibbs sampling over source locations.
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