Geoacoustic and source tracking using particle filtering: Experimental results

Abstract

A particle filtering (PF) approach is presented for performing sequential geoacoustic inversion of a complex ocean acoustic environment using a moving acoustic source. This approach treats both the environmental parameters [e.g., water column sound speed profile (SSP), water depth, sediment and bottom parameters] at the source location and the source parameters (e.g., source depth, range and speed) as unknown random variables that evolve as the source moves. This allows real-time updating of the environment and accurate tracking of the moving source. As a sequential Monte Carlo technique that operates on nonlinear systems with non-Gaussian probability densities, the PF is an ideal algorithm to perform tracking of environmental and source parameters, and their uncertainties via the evolving posterior probability densities. The approach is demonstrated on both simulated data in a shallow water environment with a sloping bottom and experimental data collected during the SWellEx-96 experiment.

Bayesian tracking of multiple acoustic sources in an uncertain ocean environment

“…The Bayesian algorithm developed here represents a simultaneous tracking approach, in that data collected at a number of times are inverted simultaneously for all source locations along a track. While potentially more intensive computationally than sequential approaches, 8 simultaneous inversion brings all data to bear on all parameters at once, providing maximal information. In this approach, environmental parameters are continually constrained by all data, and source locations at a given time are constrained by data collected at that time and at previous and later times.…”

“…Matchedfield localization/tracking methods are based on correlating complex acoustic fields observed at a sensor array with replica fields computed numerically for a grid of possible source locations. 1,2 Challenging problems in matched-field processing include environmental mismatch, 1,[3][4][5][6][7][8] and localization/tracking of multiple interfering sources. [9][10][11][12] This letter develops and illustrates a Bayesian tracking approach which addresses both of these issues.…”

Particle filtering for passive fathometer tracking

“…The filter estimates both the time-varying (or space-varying) parameters and the evolving uncertainty in these estimates by tracking the posterior probability density function (PDF) pðx t y 1 … j y t Þ. Sequential Bayesian methods have been previously used in geoacoustic inversion and relevant applications. [11][12][13][14][15][16] Passive fathometer tracking was one of the first suggested geoacoustic applications. 17 The classical PF assumes a fixed model and tracks the associated parameters.…”

Range aliasing in frequency coherent geoacoustic inversion

“…For example, for a tracking application where the previous geoacoustic parameters and source location 12,13 already are calculated and there is a good prior information about the current values, a smaller search interval width 2Dr is needed. So it is possible to use a large Df and purposefully undersample the frequency response to reduce the computational cost without sacrificing performance.…”