Bayesian Acoustic Source Track Prediction in an Uncertain Ocean Environment

This paper develops an approach for probabilistic prediction of the future locations of a moving ocean acoustic source based on probability distributions for past source locations as determined by Bayesian acoustic tracking inversion. The Bayesian track estimation for past times considers both source and environmental parameters as unknown random variables constrained by noisy acoustic data and prior information, and numerically samples the posterior probability density (PPD) using Markov-chain Monte Carlo (MCMC) methods. Applying a probabilistic prediction model for constant-velocity source motion to each of the PPD samples produces source location probability distributions for future times. These prediction distributions account for both the uncertainty of the source-motion model and the uncertainty in the state of knowledge of past source locations including the effects of environmental uncertainty. Results of Bayesian track estimation and prediction are represented as a sequence of joint marginal probability distributions over source range and depth, and as the most probable track with uncertainties. Probability distribution for the time and range of the closest point of approach (CPA) are also computed for inbound tracks. The approach is illustrated with synthetic acoustic data at two noise levels and with measured data from a shallow-water site in the Mediterranean Sea.