论文:2015,Vol:33,Issue(3):432-437
引用本文:
李晓花, 李亚安, 陈晓, 戴淼. 密集杂波环境下确定性退火DA-HPMHT跟踪算法[J]. 西北工业大学学报
Li Xiaohua, Li Yaan, Chen Xiao, Dai Miao. A Deterministic Annealing HPMHT Tracking Algorithm Suitable for Dense Clutter Environment[J]. Northwestern polytechnical university

密集杂波环境下确定性退火DA-HPMHT跟踪算法
李晓花, 李亚安, 陈晓, 戴淼
西北工业大学 航海学院, 陕西 西安 710072
摘要:
在对Homothetic概率多假设跟踪(probabilistic multiple hypothesis tracking, PMHT)和确定性退火(deterministic annealing, DA)技术深入研究的基础上,结合扩展卡尔曼算法,提出了DA-HPMHT算法。针对密集杂波环境对多目标跟踪性能的影响,给出了DA-HPMHT算法在匀速直线交叉运动目标,机动转弯目标和匀速直线邻近目标的仿真实验,并同HPMHT算法进行了仿真比较。仿真结果表明,在初始值与真实值相差较大的情况下,HPMHT算法跟踪性能下降,而DA-HPMHT算法仍能保持较好的跟踪精度,并且满足实时性要求,证明DA-HPMHT算法对密集杂波环境下多机动目标跟踪的有效性。
关键词:    概率多假设跟踪    确定性退火    多目标跟踪    扩展卡尔曼算法    密集杂波环境   
A Deterministic Annealing HPMHT Tracking Algorithm Suitable for Dense Clutter Environment
Li Xiaohua, Li Yaan, Chen Xiao, Dai Miao
College of Marine Science and Technology, Northwestern Ploytechnical University, Xi'an 710072, China
Abstract:
Based on the principle of the homothetic probabilistic multiple hypothesis tracking (HPMHT), the deterministic annealing (DA) approach, and the extended Kalman filter, we propose a new multitarget tracking algorithm DA-HPMHT. We compare and analyze the estimation accuracy of DA-HPMHT in simulation experiments for uniform linear crossover movement targets, maneuvering turning crossover targets and uniform linear closely spaced targets under the dense clutter environment. The experimental results demonstrate HPMHT algorithm's performance degrades when the initial value differs from its real value, but the DA-HPMHT algorithm still has good tracking accuracy and can meet real time requirement at the same time; this confirms the effectiveness of the DA-HPMHT in multiple maneuvering target tracking under dense clutter environment.
Key words:    probabilistic multiple hypothesis tracking (PMHT)    deterministic annealing    algorithms    multitarget tracking    extended Kalman filters    dense clutter environment    computer simulation    estimation   
收稿日期: 2014-10-28     修回日期:
DOI:
基金项目: 国家自然科学基金(51179157、51179158、51409214)资助
通讯作者:     Email:
作者简介: 李晓花(1986—),女,西北工业大学博士研究生,主要从事目标跟踪及数据融合的研究。
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参考文献:
[1] 耿峰, 祝小平. 一种改进的多传感器多目标跟踪联合概率数据关联算法研究[J]. 系统仿真学报, 2007, 19(10): 4671-4675 Geng Feng, Zhu Xiaoping. Research of Improved Joint Probabilistic Data Association Algorithm for Multisensor-Multitarget Tracking[J]. Journal of System Simulation, 2007, 19(10): 4671-4675 (in Chinese)
[2] Svensson L, Svensson D, Guerriero M, Willett P. Set JPDA Filter for Multi-Target Tracking[J]. IEEE Trans on Signal Processing, 2011, 59(10): 4677-4691
[3] 叶西宁, 潘泉, 陈鸣,等. 密集回波环境下多目标跟踪的一种新算法[J]. 西北工业大学学报, 2004, 22(3):388-391 Ye Xining, Pan Quan, Chen Ming, et al. A New and Better Algorithm for Multitarget Tracking in Dense Clutter[J]. Journal of Northwestern Polytechnical University, 2004, 22(3): 388-391 (in Chinese)
[4] 李爱军, 吴小俊. 一种模糊算法及其在多假设多目标跟踪中的应用[J].计算机工程与应用, 2008, 44(10):224-226 Li Aijun, Wu Xiaojun. Fuzzy Algorithm and Its Application to MHT Multitarget Tracking[J]. Computer Engineering and Applications,2008, 44(10):224-226 (in Chinese)
[5] Streit R L and Luginbuhl T E. A Probabilistic Multi-Hypothesis Tracking Algorithm without Enumeration and Pruning[C]//Proceedings of the Sixth Joint Service Data Fusion Symposium, 1993:1015-1024
[6] Streit R L, Luginbuhl T E. Maximum Likelihood Method for Probabilistic Multi-Hypothesis Tracking[C]//Proceedings of SPIE International Symposium, Signal and Data Processing of Small Targets, 1994, 2235: 5-7
[7] Willett P, Ruan Y, Streit R L. The PMHT: Problems and Some Solutions[J]. IEEE Trans on Aerospace and Electronic Systems, 2002, 38(3):738-754
[8] Rago C, Willett P, Streit R L. A Comparison of the JPDAF and PMHT Tracking Algorithms[C]//Proceedings of the International Conference on Acoustics, Speech and Signal Processing, 1995:357l-3574
[9] Wan F, Mak P U, Mak P I, Vai M I. Robust Deterministic Annealing Based EM Algorithm[J]. Electronics Letters, 2012,48(5): 289-290
[10] Avitzour D. A Maximum Likelihood Approach to Data Association[J]. IEEE Transa on Aerospace and Electronic Systems, 1992, 28(2): 560-565
[11] Wieneke M, Koch W. The PMHT: Solutions for Some of Its Problems[C]//Proceedings of SPIE Conference on Signal and Data Processing of Small Targets, San Diego, CA, 2007, 6699: 1-12
[12] Rago C, Willett P, Streit R L Direct Data Fusion Using the PMHT[C]//Proceedings of the 1995 American Control Conference, 1995:1688-1702
[13] Ueda N, Nakano R. Deterministic Annealing EM Algorithm[J]. Neural Networks, 1998, 11(2): 271-282
[14] David F Crouse, Marco Guerriero, Peter Willett. A Critical Look at the PMHT[J]. Journal of Advances in Information Fusion,2009, 4(2):93-116