Probability hypothesis density filter for adjacent multi-target tracking

Abstract

In the adjacent multi-target scenario, the Gaussian mixture probability hypothesis density (GM-PHD) algorithm encounters problems of inaccurate target number estimation and low tracking accuracy. To tackle these problems, this paper proposes an improved components management strategy for GM-PHD algorithm. We develop a master-slave mode to process Gaussian components, the master components whose weights exceed the extraction threshold are retained to avoid merging them each other, which guarantees the accuracy of target number estimation. Meanwhile, the slave components which satisfying the merging conditions are merged with the corresponding master components to improve the target tracking accuracy. Simulation results show that the proposed algorithm can achieve better performance than conventional GM-PHD algorithm in different clutter environments.