Improved pruning algorithm for Gaussian mixture probability hypothesis density filter

With the increment of the number of Gaussian com-ponents, the computation cost increases in the Gaussian mixture probability hypothesis density (GM-PHD) filter.Based on the the-ory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian compo-nents'means and covariance,but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algo-rithm for tracking very closely proximity targets.Moreover,it solves the end-less while-loop problem without the need of a second merging step.Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.