A GM-PHD algorithm for multiple target tracking based on false alarm detection with irregular window

Probability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. Gaussian mixture is an approximation scheme to obtain the closed solution of the PHD filter, which is only suitable for linear Gaussian case. However, when targets are moving closely to each other, GM-PHD filter cannot correctly estimate the number of targets and their states. Especially, the estimation accuracy of both target number and their states is rather difficult when targets born and disappear in closely spaced target tracking scenarios. To solve these problems, a novel multiple target tracking algorithm is proposed in this paper. For one hand, when the targets are close, a novel weight redistribution scheme of targets is proposed, which can appropriately modify the weights of the closely spaced targets so that the higher precision of state estimates can be obtained. On the other hand, we propose a false alarm detection method by using an irregular window, in which the multi-scan measurement information is considered to reduce the disturbance of clutter. In numerical experiments, the results demonstrate that the proposed approach can achieve better performance compared to the other existing methods.