Controlling track coalescence with scaled Joint Probabilistic Data Association

Track coalescence is undesirable when estimating the states of multiple manoeuvring targets, with a less-than-unity probability of detection, in clutter. Simple and compound forms of coalescence are defined and discussed. Simple coalescence is when two or more identical tracks follow a single target; compound coalescence is when two or more identical tracks follow the midpoint (or centroid) of two or more targets. It is shown that the incidence of compound track coalescence in joint probabilistic data association (JPDA) may be reduced using a scaling factor to favour the most likely association hypothesis. This prevents multiple hypothesis equivalence when tracking closely-spaced or crossing targets. The performance of the scaled JPDA (SJPDA) algorithm is compared with probabilistic data association (PDA) and JPDA using real and simulated data. Larger scaling factors decrease the likelihood and duration of compound track coalescence; however, they also increase the likelihood of track divergence on clutter or other targets. A value of unity corresponds to JPDA. The optimal value may be chosen to suit the application. A factor of two was found to give good results in the test data.