Time-Recursive Number-of-Tracks Estimation for MHT

In this paper we address the issue of measurement-to-track association within the framework of multiple hypothesis tracking (MHT). Speciically, we generate a maximum a posteriori (MAP) cost as a function of the number of tracks K. This cost is generated, for each K, as a marginalization over the set of hypothesized track-sets. The proposed algorithm is developed based on a trellis diagram representation of MHT, and a generalized list-Viterbi algorithm for pruning and merging hypotheses. Compared to methods of pruning hypotheses for either MHT or Bayesian multitarget tracking, the resulting Viterbi MHT algorithm is less likely to incorrectly drop tracks in high clutter and high missed-detection scenarios. The proposed number-of-tracks estimation algorithm provides a time-recursive estimate of the number of tracks. It also provides track estimates, allows for the deletion and addition of tracks, and accounts for false alarms and missed detections.