Weight over-estimation problem in GMP-PHD filter

The Gaussian mixture particle probability hypothesis density (GMP-PHD) filter is a promising nonlinear multi-target tracking algorithm. However, when the variance of measurement noise is small, and if there are some particles nearby clutters, the average weight of the particles will be much greater than the clutter density, because the peak value of the likelihood function is much greater than the number of particles. Therefore, the weights of Gaussian components updated by the clutter will be greater than the actual values. The present authors call this phenomenon the weight over-estimation problem, which can be solved by some modifications of the weight updating formula. Simulation results show that the proposed algorithm has better performance than the GMP-PHD filter, implying good application prospects.