An improved cubature particle PHD filter with an adaptive memory factor

Abstract

To solve the problem of multi-target tracking model with the time-varying number of targets, an improved Cubature particle PHD (CP-PHD) filter is proposed for multi-target tracking system. Firstly, the improved third-degree Spherical-Radial rule is applied to calculate the probability distribution of the nonlinear stochastic function; it introduces an adaptive memory factor for generating the importance density function based on the cubature Kalman filter (CKF). Thus the desirable particles obtained avoiding the effect of the old data. Then prediction and update the random finite set of multi-target using a bank of Gaussian particle filters. The method approximates the updated PHD into the form of Gaussian mixture using the particles with maximum. The simulation results demonstrated the proposed algorithm can effectively deal with the multi-target tracking problems with non-linear non-Gaussian model. Compared with the Gaussian particle PHD filter (GP-PHDF), the proposed algorithm can reduce the multi-target distance error by nearly 70% and reduce the running time by 20%.