Gaussian mixture probability hypothesis density filter algorithm for multi-target tracking

Abstract

Multi-target tracking is an important component of a surveillance, guidance, and obstacle avoidance system. The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown, and time varying number of targets in the presence of data association uncertainty, clutter, noise, and miss-detection. But there is no closed-form solution to the PHD recursion. Another approach to solve the problem, a closed-form solution for the PHD, named Gaussian mixture PHD (GMPHD) filter. This method can avoid the data association problem in multi-target tracking. Moreover, it is more reliable and less computational than particle PHD filter for multi-target tracking. Experiments show the GMPHD filter to be able to estimate both the number of tracked targets, as well as the states of the targets, robustly from noisy observations, the simulation results show that the method is simple and effective.