Abstract:

Taking into consideration the shortcomings of the traditional particle probability hypothesis density (PHD) filter algorithm for passive multi-target tracking, such as low accuracy, particle degradation, filter divergence, an improved multi-target tracking algorithm is proposed. In the proposed algorithm, the better importance density function is approximated by some new Gaussian distribution produced by a bunch of Gauss-Hermite filters, and the latest measurements are fully utilized. The Gauss-Hermite filters are integrated into the framework of Gaussian mixture particle PHD (GMP-PHD), which solves the nonlinear problem and improves the accuracy of the proposed algorithm for passive multi-target tracking. Simulations show that the proposed algorithm has higher precision than the conventional GMP-PHD method, and it effectively decreases the loss rate of target estimates.