Tracking a Broadband Acoustic Source in the Near Field using a Particle Filter


  1. Birsan, M.
Corporate Authors
Defence R&D Canada - Atlantic, Dartmouth NS (CAN)
This report describes a numerical method that may be used to efficiently locate and track underwater sonar targets in the near field for the case of very small passive arrays. The waves emitted by the target are considered as curved rather than plane. The method uses only two pairs of acoustic sensors and is a two-step process. In the first step the time-delays are estimated for each pair of hydrophones using the generalised cross-correlation function. The second step consists of fusing this information based on the known geometry of the array to come up with the best estimate of target position. The optimal solution to the real time positioning problem is given by the recursive Bayesian filter. A transition equation describes the prior distribution of the desired parameters (target position and velocity), the so-called hidden state process, and an observation equation describes the likelihood of the observations (measurements). The determination of target position and velocity is formulated as an optimal stochastic estimation problem, which could be solved using a sequential Monte Carlo based approach known as particle filter. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses the unscented Kalman filter (UKF) to generate the prior distribution of the unknown parameters. Finally, it is demonstrated the ability of the approach to track a fishing vessel over a period of time using the data collected by the Rapidly Deploymen

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tracking and classification;acoustic source;recursive Bayesian filter;particle filter;unscented Kalman filter;tracking algorithm;generalized cross-correlation
Report Number
DRDC-ATLANTIC-TM-2005-079 — Technical Memorandum
Date of publication
01 Sep 2005
Number of Pages
Electronic Document(PDF)

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