A Scaled Joint Probability Data Association Algorithm

A modified Joint Probabilistic Data Association algorithm is proposed in this paper to avoid track coalescence. Above all, an arbitrary positive scaling factor will be employed to multiply the maximum probabilities of every target associated with measurements. Then an exclusive measurement is defined for every target in the new algorithm, which is the maximum probability measurement associated with the target. The association probabilities of exclusive measurement with other targets except corresponding target are set at 0. At last, the association probabilities of every measurement will be given weights by means of the Entropy Value Method in the new algorithm. The simulation results show that the new algorithm can effectively solve the track coalescence problem in all kinds of scenarios and its track performance is better than the Joint Probabilistic Data Association algorithm´s.