Particle filtering for arrival time tracking in space and source localization.

Locating and tracking a source in an ocean environment and estimating environmental parameters of a sound propagation medium are critical tasks in ocean acoustics. Many approaches for both are based on full field calculations which are computationally intensive and sensitive to assumptions on the structure of the environment. Alternative methods that use only select features of the acoustic field for localization and environmental parameter estimation have been proposed. The focus of this paper is the development of a method that extracts arrival times and amplitudes of distinct paths from measured acoustic time-series using sequential Bayesian filtering, namely, particle filtering. These quantities, along with complete posterior probability density functions, also extracted by filtering, are employed in source localization and bathymetry estimation. Aspects of the filtering methodology are presented and studied in terms of their impact on the uncertainty in the arrival time estimates. Using the posterior probability densities of arrival times, source localization and water depth estimation are performed for the Haro Strait Primer experiment; the results are compared to those of conventional methods. The comparison demonstrates a significant advantage in the proposed approach.