Multi-target tracking using joint probabilistic data association

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Abstract
The Probabilistic Data Association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, some new theoretical results are presented on the Joint Probabilistic Data Association (JPDA) algorithm, in which joint posterior probabilities are computed for multiple targets in Poisson clutter. The algorithm is applied to a passive sonar tracking problem wlth multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e. low) probabilities of detection at each sample time. Simulation results are presented for two heavily interfering targets; these illustrate the dramatic improvements obtained by computing joint probabilities.

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