Gaussian mixture particle flow probability hypothesis density filter

Abstract

The probability hypothesis density (PHD) filter is a promising filter for multi-target tracking which propagates the posterior intensity of the multi-target state. In this paper, a Gaussian mixture particle flow PHD (GMPF-PHD) filter is proposed which uses a bank of particles to represent the Gaussian components in the Gaussian mixture PHD (GM-PHD) filter. Then a particle flow is implemented to migrate the particles to a more appropriate region in order to obtain a more accurate approximation of the posterior intensity. To verify the effectiveness of the algorithm, both linear and nonlinear multi-target tracking problem are designed, and the performance are compared with the classical approaches such as the GM-PHD filter, the Gaussian mixture particle PHD (GMP-PHD) filter, and the particle PHD filter. Simulation results show that the proposed filter can achieve a good performance with a reasonable computational cost.