Joint probabilistic data association avoiding track coalescence

Abstract

For the problem of tracking multiple targets, the joint probabilistic data association (JPDA) filter has been shown to be very effective in handling clutter and missed detections. The JPDA, however, also tends to coalesce neighbouring tracks. Through comparing JPDA with the exact nearest-neighbour probabilistic data association (ENNPDA) filter, R.J. Fitzgerald (1990) has shown that hypothesis pruning is an effective way to prevent track coalescence. The dramatic pruning used for ENNPDA, however, leads to an undesired sensitivity to clutter and missed detections. In this paper, a new algorithm combines the advantages of both JPDA and ENNPDA. (3 pages)