Performance evaluation of MHT and GM-CPHD in a ground target tracking scenario

Performance evaluations of multi-target tracking algorithms are often limited to consider comparisons within the same algorithm family. In this paper, two conceptually different multi-target tracking algorithms are evaluated, namely a multiple-hypothesis tracking (MHT) algorithm and the Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter. As a reference, a conventional single-hypothesis tracking algorithm is included in the evaluation. The performance is assessed using the root-mean square error of the estimated number of targets, and the recently published optimal subpattern assignment (OSPA) measure. The scenario under consideration is tracking of nine closely spaced ground targets, using simulated measurements from an airborne radar. By observing the estimation of the number of targets, as well as of the target states, conclusions are drawn regarding the behavior of MHT and GM-CPHD. For example, GM-CPHD is more responsive to changes in the number of targets, whereas MHT is less responsive, but produces a more stable output.