For the nonlinear multi-target tracking problem, the heavy-tailed process and measurement noises can reduce the performance of the Gaussian mixture particle probability hypothesis density(GMP-PHD) filter severely. To solve this problem, this paper proposed a new student's t mixture particle probability hypothesis density filter(STMP-PHD). The method used a student's t model to approximate the process noise and the measurement noise, and used a student's t mixture model to approximate the intensity of the multi-target. The algorithm made the Monte Carlo method to calculate the student's t integral, and establish the student's t mixture closed recursive framework. The simulation results confirmed that the filter can effectively overcome the negative effects of the heavy-tailed process noise and the measurement noise, and maintain the high tracking precision.

[2020-06-05] Printed Article /doc/2018.11.0905/v2