Cross entropy algorithms for data association in multi-target tracking

Multiple-target tracking (MTT) poses difficult computational challenges related to the measurement-to-track data association problem, especially in the presence of spurious and missing measurements. Different approaches have been proposed to tackle this problem, including various approximations and heuristic optimization tools. The cross entropy (CE) method and the related parametric MinxEnt (PME) method are recent optimization heuristics that have proved useful in many combinatorial optimization problems. They are akin to evolutionary algorithms in that a population of solutions is evolved, however generation of new solutions is based on statistical methods of sampling and parameter estimation. In this work we apply the CE method and its recent MinxEnt variant to the multi-scan version of the data association problem in the presence of misdetections, false alarms, and unknown number of targets. We formulate the algorithms, explore via simulation their efficiency and performance compared with other recently proposed techniques, and show that they obtain state-of-the-art performance in challenging scenarios.