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Comparison of PMHT and S-D assignment trackers

The S-dimensional (S-D) assignment algorithm is a recently- favored approach to multitarget tracking in which the data association is formulated as a generalized multidimensional matching problem, and solved by a Lagrangian (dual) relaxation approach. The Probabilistic Multiple Hypothesis Tracking algorithm is a relatively new method, which uses the EM algorithm and a modified probabilistic model to develop a `soft' association tracker. In this paper, we implement the two algorithms (S = 3, in the S-D assignment algorithm) in the multitarget tracking problem, presented with false alarms and imperfect target detection. Simulation results for various scenarios are presented and the performances of the two algorithms are compared in terms of computational time and percentage of lost tracks.