Time-Recursive Number-of-Tracks Estimation for MHT

Jessica Bradley, Kevin Buckley and Richard Perry
ECE Department, Villanova University, Villanova PA 19085

Abstract:

In this paper we address the issue of measurement-to-track association within the framework of multiple hypothesis tracking (MHT). Specifically, 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.

Keywords: Multiple Hypothesis Tracking, MHT, multiple target tracking, number-of-track estimation, Maximum A Posterior estimation, MAP

Further author information: K.B.: E-mail: buckley@ece.vill.edu; 610-519-5658, FAX: (610)-519-4436

This research was supported by ONR under Grant N00014-98-1-0892.