一种新的避免航迹合并的联合综合概率数据关联滤波器

doi:10.11999/JEIT170085

Abstract
Figure/Table
References (15)
Related Citation (15)

Download: PDF (484 KB)
Export: BibTeX | EndNote (RIS)

Abstract To avoid the track coalescence of the Joint Integrated Probabilistic Data Association (JIPDA), a modified version of JIPDA is proposed by modelling targets as Random Finite Set (RFS). The JIPDA first generates the original Probability Density Function (PDF) and then makes an approximation of the PDF to estimate target states. To maximize the similarity between the state estimate PDF and the original PDF, the original PDF is optimized when target label is irrelevant. Using the KL divergence as a measure of the similarity, the cost function is developed. The experimental results show that the proposed method can effectively avoid the track coalescence.

Key words： Multi-target tracking Joint Integrated Probabilistic Data Association (JIPDA) Random Finite Set (RFS)

Received: 23 January 2017 Published: 22 August 2017

PACS:

TN958.97

Fund:The National Natural Science Foundation of China (61401526), The Foundation of National Ministries (9140A07020614DZ01)

Corresponding Authors: ZHU Yun E-mail: yunzhuxidian@163.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHU Yun
	WANG Jun
	CHEN Gang
	GUO Shuai

Cite this article:

ZHU Yun,WANG Jun,CHEN Gang等. Novel Track Coalescence Avoiding Joint Integrated Probabilistic Data Association Filter[J]. JEIT, 2017, 39(10): 2346-2353.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT170085 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I10/2346

[1]	CHANG K C and BAR-SHALOM Y. Joint probabilistic data association for multitarget tracking with possibly unresolved measurements and maneuvers[J]. IEEE Transactions on Automatic Control, 1984, 29(7): 585-594. doi: 10.1109/TAC. 1984.1103597.
[2]	MUŠICKI D and EVANS R. Joint integrated probabilistic data association-JIPDA[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(3): 1093-1099. doi: 10.1109/ TAES.2004.1337482.
[3]	尹帅, 袁俊泉, 吴顺华, 等. 一种改进的JIPDA多目标跟踪算法[J]. 雷达科学与技术, 2014, 12(3): 285-290. doi: 10.3969/ j.issn.1672-2337.2014.03.011.
	YIN Shuai, YUAN Junquan, WU Shunhua, et al. An improved JIPDA algorithm for multi-target tracking[J]. Radar Science and Technology, 2014, 12(3): 285-290. doi: 10.3969/j.issn.1672-2337.2014.03.011.
[4]	伍明, 李琳琳, 魏振华, 等. 一种未知环境下机器人多目标跟踪算法[J]. 智能系统学报, 2015, 10(3): 448-453. doi: 10.3969/ j.issn.1673-4785.201405051.
	WU Ming, LI Linlin, WEI Zhenhua, et al. A robot multi- object tracking algorithm in unknown environments[J]. CAAI Transactions on Intelligent Systems, 2015, 10(3): 448-453. doi: 10.3969/j.issn.1673-4785.201405051.
[5]	CHEN Xin, PELLETIER M, KIRUBARAJAN T, et al. Integrated Bayesian clutter estimation with JIPDA/MHT trackers[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 395-414. doi: 10.1109/TAES.2013. 6404111.
[6]	BLOM H A P, BLOEM E A, and MUŠICKI D. JIPDA*: Automatic target tracking avoiding track coalescence[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(2): 962-974. doi: 10.1109/TAES.2014.130327.
[7]	MAHLER R. Statistical Multisource Multitarget Information Fusion[M]. London, Artech House, 2007: 5-14.
[8]	WILLIAMS J L. Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 1664-1687. doi: 10.1109/TAES.2015.130550.
[9]	余艳. 融合KL散度和移地距离的高斯混合模型相似性度量方法[J]. 计算机应用, 2014, 34(3): 828-832. doi: 10.11772/j.issn. 1001-9081.2014.03.0828.
	YU Yan. Similarity measure method of Gaussian mixture model by integrating Kullback-Leibler divergence and earth mover's distance[J], Journal of Computer Applications, 2014, 34(3): 828-832. doi: 10.11772/j.issn.1001-9081.2014.03.0828.
[10]	BLAHUT R E. Principles and Practice of Information Theory[M]. MA: Addison-Wesley, 1987, Chapter 7.
[11]	SVENSSON L, SVENSSON D, and WILLETT P. Set JPDA algorithm for tracking unordered sets of targets[C]. 12th International Conference on Information Fusion, Seattle, WA, USA, 2009: 1187-1194.
[12]	SCHUHMACHER D, VO B T, and VO B N. A consistent metric for performance evaluation of multi-object filters[J]. IEEE Transactions on Signal Processing, 2008, 56(8): 3447-3457. doi: 10.1109/TSP.2008.920469.
[13]	MUŠICKI D and EVANS R. Clutter map information for data association and track initialization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(2): 387-398. doi: 10.1109/TAES.2004.1309992.
[14]	JING P L, XU S Y, LI X, et al. Coalescence-avoiding joint probabilistic data association based on bias removal[J]. EURASIP Journal on Advances in Signal Processing, 2015(1): 1-13. doi: 10.1186/s13634-015-0205-2.
[15]	PANAKKAL V P and VELMURUGAN R. Effective joint probabilistic data association using maximum a posteriori estimates of target states[C]. 16th International Conference on Information Fusion, Istanbul, Turkey, 2013: 781-788.