Sound speed estimation and source localization with linearization and particle filtering

Abstract

A method is developed for the estimation of source location and sound speed in the water column relying on linearization. The Jacobian matrix, necessary for the proposed linearization approach, includes derivatives with respect to empirical orthogonal function coefficients instead of sound speed directly. First, the inversion technique is tested on synthetic arrival times, using Gaussian distributions for the errors in the considered arrival times. The approach is efficient, requiring a few iterations, and produces accurate results. Probability densities of the estimates are calculated for different levels of noise in the arrival times. Subsequently, particle filtering is employed for the estimation of arrival times from signals recorded during the Shallow Water 06 experiment. It has been shown in the past that particle filtering can be employed for the successful estimation of multipath arrival times from short-range data and, consequently, in geometry, bathymetry, and sound speed inversion. Here probability density functions of arrival times computed via particle filtering are propagated backward through the proposed inversion process. Inversion estimates are consistent with values reported in the literature for the same quantities. Last it is shown that results are consistent with estimates resulting from fast simulated annealing applied to the same data.