Particle filter source tracking in an uncertain environment

Abstract

Abstract: This paper addresses the tracking of acoustic source parameters such as depth, range and speed in spatially and temporally changing ocean acoustic environments. Conventional matched-field processing requires an accurate knowledge of environmental parameters (e.g. sound speed profile (SSP), water depth, sediment and bottom parameters) for source localization. Since environmental mismatch may result in significant errors in source parameters, algorithms that minimize this mismatch should be employed. For this purpose, a particle filtering (PF) approach is adopted here where the geoacoustic parameters are tracked simultaneously with the source location and ship speed in a range-dependent environment. 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 source and environmental parameters, and their uncertainties via the evolving posterior probability densities. The algorithm is demonstrated on simulated data in a sloping bottom environment with SSP, water depth and sediment parameters evolving as the ship moves. PACS numbers: 43.30.Pc, 43.60.Pt, 43.60.Wy

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Cite this paper

@inproceedings{Yardim2009ParticleFS, title={Particle filter source tracking in an uncertain environment}, author={Caglar Yardim and Peter Gerstoft and William S. Hodgkiss}, year={2009} }