Abstract
Tracking methods attempt to follow the movement of a target of interest while suppressing irrelevant clutter. A particularly troublesome source of clutter is wakes that appear behind the target. This problem arises in sonar tracking of human divers, in the tracking of boats using surveillance radars, and also in radar tracking of ballistic missiles. Previous research has integrated a solution to this problem in the popular Probabilistic Data Association filter (PDAF). This paper proposes a new solution to this problem in the same framework. While previous research has used an approach described as probabilistic editing, the new solution solves the wake problem in a Bayesian framework by means of marginalization. Monte-Carlo simulations show that the new solution provides significantly increased robustness as compared to both the standard PDAF and the probabilistic editing approach. As the new solution has improved theoretical underpinnings, we hope that it can be useful for further research on tracking in the presence of wake clutter.
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