Bayesian source track prediction in an uncertain environmental inversion
Keywords:Acoustic noise measurement, Bayesian networks, Oceanography, Probability density function, Random processes, Random variables, Acoustic data, Acoustic sources, Bayesian, Environmental parameter, Marginal probability, Moving acoustic sources, Posterior probability, Probabilistic prediction, Radial velocity, Source tracking, Uncertain environments
AbstractA study was conducted to consider probabilistic prediction of the potential locations of a moving acoustic source in the ocean based on earlier locations determined by Bayesian source tracking in an uncertain environment. The Bayesian tracking approach considered both source and environmental parameters as unknown random variables constrained by posterior probability density (PPD) over the environmental parameters. The approach considered source and environmental parameters to obtain a time-ordered series of joint marginal probability surfaces over source range and depth. Acoustic data were measured at 300 Hz at a vertical array consisting of 24 sensors at 4-m spacing from 26- to 118-m depth. The track consisted of an acoustic source at 30-m depth moving toward the array at a constant radial velocity of 5 m/s.
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