J4 ›› 2011, Vol. 38 ›› Issue (1): 117-122.doi: 10.3969/j.issn.1001-2400.2011.01.019

• 研究论文 • 上一篇    下一篇



  1. (西安电子科技大学 电子工程学院,陕西 西安   710071)
  • 收稿日期:2010-01-05 出版日期:2011-02-20 发布日期:2011-04-08
  • 通讯作者: 张俊根
  • 作者简介:张俊根(1979-),男,西安电子科技大学博士研究生,E-mail: zhang_jungen@sina.com.
  • 基金资助:


Parallel Gaussian particle filter for passive multi-target tracking

ZHANG Jungen;JI Hongbing;CAI Shaoxiao   

  1. (School of Electronic Engineering, Xidian Univ., Xi'an  710071, China)
  • Received:2010-01-05 Online:2011-02-20 Published:2011-04-08
  • Contact: ZHANG Jungen



关键词: 多目标跟踪, 数据关联, 高斯粒子滤波, 马尔可夫链蒙特卡罗


In multi-target tracking, aiming at the data association problem and the curse of dimensionality that arises due to the increased size of the state-space associated with multiple targets, a novel algorithm based on the parallel Gaussian particle filter (parallel-GPF) is proposed. By assuming that the states of the targets conditioned on the past measurements are mutually independent, the algorithm is amenable for multi-channel parallel implementation. With the Markov chain Monte Carlo (MCMC) approach the marginal association probabilities can be evaluated. Then, the filtering distributions of each target are computed independently with GPF. And, Quasi Monte Carlo (QMC) integration is utilized for approximating the prediction and update distributions. Finally, the proposed method is applied to passive multi-sensor multi-target tracking. Simulation results show that the proposed algorithm can obtain a better tracking performance than JPDAF, MCMCDAF and MC-JPDAF.

Key words: multi-target tracking, data association, Gaussian particle filter, Markov chain Monte Carlo