Improved pruning algorithm for Gaussian mixture probability hypothesis density filter
Department of Automation, Tsinghua University, Beijing 100084, China, Yongfang Nie, Tao Zhang, Department of Strategic Missile and Underwater Weapon, Naval Submarine Academy, Qingdao 266071, China · Journal of Systems Engineering and Electronics · 2018
With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density (GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components' means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm 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.