SUBOPTIMAL JOINT PROBABILISTIC DATA ASSOCIATION

A significant problem in multiple target tracking is the hit-to-track data association. A hit is a received signal from a target or background clutter which provides positional information. If an incorrect hit is associated with a track, that track could diverge and terminate. Prior methods for this data association problem include various optimal and suboptimal two-dimensional assignment algorithms which make hit-to-track associations. Another method is to assign a weight for the reasonable hits and use a weighted centroid of those hits to update the track. This method of weighting the hits is known as joint probabilistic data association (JPDA). This paper reviews the JPDA approach and a simple ad hoc approximation and then introduces a new suboptimal JPDA algorithm. Examples are given which compare an optimal two-dimensional assignment algorithm with the ad hoc and this new suboptimal JPDA formulations.