Addressing Track Hypothesis Coalescence in Sequential $K$-Best Multiple Hypothesis Tracking

Multiple hypothesis tracking (MHT) is generally the preferred data association technique for tracking targets in clutter and with missed detections because of its increased accuracy over conventional single-scan techniques such as nearest neighbor (NN) and probabilistic data association (PDA). However, this improved accuracy comes at the price of greater complexity. Sequential
$K$
-best MHT is a simple implementation of MHT that attempts to achieve the accuracy of MHT with some of the simplicity of single-frame methods. Our first major objective is to determine under what general conditions sequential
$K$
-best data association is preferable to PDA. Both methods are implemented for a single-target, single-sensor scenario in two spatial dimensions. Using the track loss ratio as our primary performance metric, we compare the two methods under varying false alarm densities and missed-detection probabilities. Upon implementing a single-target sequential
$K$
-best MHT tracker, a fundamental problem was observed in that the track hypotheses coalesce. The second major thrust of this research is to compare different approaches to resolve this issue. Several methods to detect track hypothesis coalescence, mostly based on the Mahalanobis and Kullback-Leibler distances, are presented and compared. Surprisingly, the most effective method to deal with track hypothesis coalescence was simply to not let different track hypotheses pick the same measurement during data association.