Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (2): 229-235.doi: 10.21629/JSEE.2018.02.02

• Electronics Technology • Previous Articles     Next Articles

Improved pruning algorithm for Gaussian mixture probability hypothesis density filter

Yongfang NIE1,2(), Tao ZHANG1,*()   

  1. 1 Department of Automation, Tsinghua University, Beijing 100084, China
    2 Department of Strategic Missile and Underwater Weapon, Naval Submarine Academy, Qingdao 266071, China
  • Received:2017-03-07 Online:2018-04-26 Published:2018-04-27
  • Contact: Tao ZHANG;
  • About author:NIE Yongfang was born in 1976. She received her M.S. degree in weapon system engineering from Naval Aeronautical Engineering Institute in 2002. She was a lecturer with Naval Submarine Academy from 2002 to 2014. Now, she is pursuing her Ph.D. degree at Tsinghua University. Her current research activity focuses on nonlinear filtering and adaptive control. E-mail:|ZHANG Tao was born in 1969. He received his B.S. degree, M.S. degree and Ph.D. degree from Tsinghua University, Beijing, China, in 1993, 1995 and 1999 respectively. He received his second Ph.D. degree from Saga University, Saga, Japan, in 2002. He is currently a professor and the deputy head of the Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China. He is the author or coauthor of more than 200 papers and three books. His current research includes robotics, control theory, artificial intelligent, navigation and control of spacecraft, fault diagnosis and reliability analysis, body signal extraction and recognition. E-mail:
  • Supported by:
    the National Natural Science Foundation of China(61703228);This work was supported by the National Natural Science Foundation of China (61703228)


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.

Key words: Gaussian mixture probability hypothesis density (GMPHD) filter, pruning algorithm, proximity targets, clutter rate