J4 ›› 2010, Vol. 37 ›› Issue (2): 218-223.doi: 10.3969/j.issn.1001-2400.2010.02.007

• 研究论文 • 上一篇    下一篇



  1. (1. 西安电子科技大学 电子工程学院,陕西 西安  710071;
    2. 防空兵指挥学院 信息控制系,河南 郑州  450052)
  • 收稿日期:2009-03-17 出版日期:2010-04-20 发布日期:2010-06-03
  • 通讯作者: 时银水
  • 作者简介:时银水(1965-),男,副教授,西安电子科技大学博士研究生,E-mail: zz_sys@163.com.
  • 基金资助:


Multiple passive-radar based time-varying number targets tracking algorithm

SHI Yin-shui1,2;JI Hong-bing1;YANG Bai-sheng1   

  1. (1. School of Electronic Engineering, Xidian Univ., Xi'an  710071, China;
    2. Inteligence and Control, Air Defense Forces Academy, Zhengzhou  450052, China)
  • Received:2009-03-17 Online:2010-04-20 Published:2010-06-03
  • Contact: SHI Yin-shui



关键词: 无源雷达, 随机有限集, 概率假设密度, 多目标跟踪, 数据关联


A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states through passive radar measurements. Firstly, multi-sensor central fusion scheme is adopted to improve the weak observability for passive systems. Secondly, the least square method is embedded to calculate pseudo-location measurements by which the nonlinearity is solved. Thirdly, for the scenario of the time-varying target number, the new approach involves modeling the collections of targets and measurements as random finite sets (RFSs), respectively, and applying the Gaussian mixture probability hypothesis density (GMPHD) recursion to propagate the posterior intensity, which is a first-order statistic of the random finite sets by which both the time-varying number and states of multiple targets could be estimated properly. Furthermore, data association is accomplished by all potential targets located by the least square algorithm, which could avoid the decrease of association reliability when lines of sight (LOS) from different targets are close to each other. Simulation results in a scenario of tracking targets through multiple passive sensors show the advantages of the proposed algorithm.

Key words: passive radar, random finite sets, probability hypothesis density(PHD), multiple targets tracking, data association