Particle filter source tracking in a changing geoacoustic environment.

Abstract

This paper addresses the problem of tracking the acoustic source parameters such as the depth, range, and speed in evolving geoacoustic environments. It is well known that inaccurate knowledge about the environmental parameters such as the sound speed profile (SSP), water depth, sediment, and bottom parameters may result in significant errors in source parameters. To counter this, a particle filtering (PF) approach is adopted here where the geoacoustic parameters are tracked together with the source location and speed in a range-dependent environment. This allows accurate, real-time updating of the environment the ship is moving in and hence source can be located at any time accurately. As a sequential Monte Carlo technique that can operate on nonlinear systems with non-Gaussian probability densities, the PF is an ideal tracking algorithm to perform tracking of source and environmental parameters and their evolving probability distributions. The algorithm is tested on a sloping environment with the SSP, water depth, and sediment parameters evolving as the ship moves. The change in the water depth created the well-known “source mirage effect,” but the PF was still able to track the true source, geoacoustic parameters, and their evolving densities in this spatially varying environment. [Work supported by ONR.]