采用粒子滤波和模糊聚类法的非线性多目标跟踪

doi:10.3969/j.issn.1001-2400.2010.04.010

J4 ›› 2010, Vol. 37 ›› Issue (4): 639-641.doi: 10.3969/j.issn.1001-2400.2010.04.010

采用粒子滤波和模糊聚类法的非线性多目标跟踪

张俊根;姬红兵

(西安电子科技大学电子工程学院，陕西西安 710071)

收稿日期:2009-06-13 出版日期:2010-08-20 发布日期:2010-10-11
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:
国家自然科学基金资助项目(60871074)

Passive multi-target tracking based on independent particle filtering and fuzzy clustering

ZHANG Jun-gen;JI Hong-bing

(School of Electronic Engineering, Xidian Univ., Xi'an 710071， China)

Received:2009-06-13 Online:2010-08-20 Published:2010-10-11
Contact: ZHANG Jun-gen

摘要/Abstract

摘要：

提出一种新的非线性多目标跟踪方法，用模糊聚类算法实现数据关联，采用粒子滤波实现对各目标的独立跟踪．首先利用最大熵模糊聚类对目标和观测数据进行关联，采用模糊隶属度重建多目标滤波中的联合关联概率矩阵．然后利用粒子滤波适于处理非线性问题的特点，通过联合关联信息，采用粒子滤波独立对各目标进行滤波，实现对目标状态的更新．最后，将该算法应用于多传感器多目标纯方位角跟踪．仿真结果表明，相比于联合概率数据关联算法及MEF-JPDAF，新算法具有更高的跟踪精度．

关键词: 非线性多目标跟踪, 数据关联, 最大熵模糊聚类, 独立粒子滤波, 纯方位角跟踪

Abstract:

A novel method based on fuzzy clustering and independent particle filtering is proposed for nonlinear multi-target tracking. Firstly, the association of target with measurement is carried out by the use of the maximum entropy fuzzy clustering. Then the joint association probability matrix is reconstructed by utilizing the fuzzy membership degree of the target and measurement. Since particle filtering performs well in the nonlinear tracking system, this paper employs it and the joint association innovations to update each target state independently. Finally, the proposed method is applied to multi-sensor multi-target bearings-only tracking. Simulation results show that the method can obtain a higher tracking precision than JPDAF and MEF-JPDAF.

Key words: nonlinear multi-target tracking, data association, maximum entropy fuzzy clustering, independent particle filtering, bearings-only tracking

张俊根;姬红兵. 采用粒子滤波和模糊聚类法的非线性多目标跟踪[J]. J4, 2010, 37(4): 639-641.

ZHANG Jun-gen;JI Hong-bing. Passive multi-target tracking based on independent particle filtering and fuzzy clustering[J]. J4, 2010, 37(4): 639-641.

参考文献

[1] Bar-Shalom Y. Multitarget-multisensor Tracking: Applications and Advances[M]. Norwood: Artech House, 1990.
[2] Blackman S S. Multiple Hypotheses Tracking for Multiple Target Tracking[J]. IEEE AES MAG, 2004, 19(1): 5-18.
[3] Mourad O, De Schutter J. Hybrid Fuzzy Probabilistic Data Association Filter and Joint Probabilistic Data Association Filter[J]. Information Sciences, 2002, 142(1-4):195-226.
[4] Liu P X, Meng M Q -H. Online Data-driven Fuzzy Clustering with Applications to Real-time Robotic Tracking[J]. IEEE Trans on Fuzzy Systems, 2004, 12(4):516-523.
[5] 张怀根, 吴顺君, 张林让, 等. 基于协方差矩阵元素的多目标角跟踪方法[J]. 西安电子科技大学学报, 2008, 35(5):785-792.
Zhang Huaigen, Wu Shunjun, Zhang Linrang, et al. Multiple Targets Angle Tracking Algorithm Based on the Elements of the Covariance Matrix[J]. Journal of Xidian University, 2008, 35(5):785-792.
[6] 刘进忙, 姬红兵, 左涛. 纯方位观测的航迹不变量目标跟踪方法[J]. 西安电子科技大学学报, 2008, 35(1):49-53.
Liu Jinmang, Ji Hongbing, Zuo Tao. Trajectory Invariable-information Target Tracking Algorithm with Bearing-only Measurement[J]. Journal of Xidian University, 2008, 35(1): 49-53.
[7] Cappe O, Godsill S J, Moulines E. An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo[J]. Proceedings of the IEEE, 2007, 95(5): 899-924.
[8] 孙伟, 郭宝龙. 一种伪粒子滤波的多目标跟踪方法[J]. 西安电子科技大学学报, 2008, 35(2): 248-253.
Sun Wei, Guo Baolong. Multiple Objects Detecting and Tracking with the Pseudo Particle Filter[J]. Journal of Xidian University, 2008, 35(2):248-253.
[9] 辛云宏, 杨万海. 被动多站多目标的测量数据关联算法研究[J]. 宇航学报, 2005, 26(6): 748-797.
Xin Yunhong, Yang Wanhai. A Method of the Passive Multi-sensor Multi-target Measurement Data Association[J]. Journal of Astronautics, 2005, 26(6): 748-797.
[10] Li Liangqun, Ji Hongbing, Gao Xinbo. Maximum Entropy Fuzzy Clustering with Application to Real-time Target Tracking[J]. Signal Processing, 2006, 86(11):3432-3447.
[11] Ekman M, Sviestins E, Sjoberg L. Particle Filters for Tracking Closely Spaced Targets[C]//Proc of FUSION 2007 Conf. New York: Inst of Elec and Elec Eng Computer Society, 2007:1-8.
[12] van der Merwe R, Doucet A, de Freitas N, et al. The Unscented Particle Filter[R]. Cambridge: Cambridge University Engineering Department, 2000.

采用粒子滤波和模糊聚类法的非线性多目标跟踪

Passive multi-target tracking based on independent particle filtering and fuzzy clustering

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

Metrics

本文评价

推荐阅读 0

[1]	韩卓茜,王锋,陈沛,李卓伦. 一种历史特征辅助的模糊数据关联算法[J]. 西安电子科技大学学报, 2021, 48(2): 92-98.
[2]	李翠芸,李洋,姬红兵,石仁政. 一步数据关联GLMB扩展目标跟踪算法[J]. 西安电子科技大学学报, 2020, 47(5): 137-143.
[3]	靳标;苏涛;李艳艳;张龙. 一种高分辨距离像辅助的多目标跟踪算法[J]. J4, 2016, 43(1): 1-6+29.
[4]	张俊根;姬红兵;蔡绍晓. 并行高斯粒子滤波被动多目标跟踪新算法[J]. J4, 2011, 38(1): 117-122.
[5]	杨柏胜;姬红兵;高小东. 随机集粒子滤波的快速被动数据关联算法[J]. J4, 2010, 37(4): 655-659.
[6]	时银水;姬红兵;杨柏胜. 组网无源雷达变数目多目标跟踪算法[J]. J4, 2010, 37(2): 218-223.
[7]	李良群;姬红兵. 基于最大熵模糊聚类的快速数据关联算法[J]. J4, 2006, 33(2): 251-256.
[8]	何友;衣晓;关欣. 基于串行处理的动态多维分配算法[J]. J4, 2005, 32(3): 489-493.
[9]	暂时无作者信息. 杂波环境下神经网络数据关联算法[J]. J4, 1999, 26(3): 310-315.
[10]	刘源;谢维信;杜文吉. 模糊-概率交互作用的数据关联算法[J]. J4, 1999, 26(2): 0-0.