基于信息熵权的最近邻域数据关联算法

doi:10.3969/j.issn.1001-506X.2020.04.10

Abstract

Abstract:

The radar data association algorithm in the complex environment is one of the most difficult problems in the field of multi-target tracking. Among them, the nearest neighbor algorithm is an effective data association algorithm with small computation and easy application. However, there is a problem that the data association accuracy is not high, the filtering result is not accurate enough, and the multi-target tracking is easy to cause error correlation. In order to improve the data association effect of the algorithm, a nearest neighbor data association algorithm is proposed. By further deepening the entropy of known measurement information, the entropy weight method is used to analyze and determine the weights of the respective measurement indicators. The weight is optimized for the statistical distance association criterion of the nearest neighbor algorithm, thereby improving the problem of the original algorithm in single target tracking. The simulation results show that the improved algorithm proposed has higher data association accuracy, smaller tracking error and faster convergence than the original algorithm.

Key words: data association, information entropy, nearest neighbor, entropy weight, statistical distance

CLC Number:

TN957.52

Henglu LI, Baixiao CHEN, Yi DING, Zhaoming ZHANG. Nearest neighbor data association algorithm based on information entropy weight[J]. Systems Engineering and Electronics, 2020, 42(4): 806-812.

Figures/Tables 4

References 34

1	何友, 修建娟, 关欣. 雷达数据处理及应用[M]. 北京: 电子工业出版社, 2013.
	HE Y , XIU J J , GUAN X . Radar data processing and application[M]. Beijing: Electronic Industry Press, 2013.
2	DANIYAN A, ALDOWESH A, GONG Y, et al.Data association using game theory for multi-target tracking in passive bistatic radar[C]//Proc.of the IEEE Radar Conference, 2017: 42-46.
3	LI N, ZHANG Z, AN D, et al.A distributed UKF method based on improved joint probability data association[C]//Proc.of the 36th Chinese Control Conference, 2017: 5306-5311.
4	PU Y Q, LIU H, CHEN S H.A data correlation algorithm based on resnet for unsteady data[C]//Proc.of the 7th International Conference on Information, Communication and Networks, 2019: 219-224.
5	WANG S L, BI D P, RUAN H L, et al.Optimized radar data association algorithm inspired by human cognition mechanism[C]//Proc.of the IEEE International Conference on Automation, Electronics and Electrical Engineering, 2018: 54-59.
6	QIU W, WANG W K, ZHUANG Z Y, et al.Using ship radiated noise spectrum feature for data association in underwater target tracking[C]//Proc.of the 20th International Conference on Information Fusion, 2017: 1-5.
7	HONG D, HEO G, MYUNG C, et al.Data association approach to mobile robot localization using non-unique landmarks[C]//Proc.of the 17th International Conference on Control, Automation and Systems (ICCAS), 2017: 1049-1053.
8	PAN S, BAO Q, HOU W, et al. An improved track segment association algorithm using MM-GNN method[C]//Proc.of the Progress in Electromagnetics Research Symposium, 2017: 675-681.
9	DUENAS A G, JOERGER M, SPENKO M.Local nearest neighbor integrity risk evaluation for robot navigation[C]//Proc.of the IEEE International Conference on Robotics and Automation, 2018: 2328-2333.
10	ZHANG K J, LIN H S, LIU G, et al.Improved interacting multiple model-new nearest neighbor data association algorithm[C]//Proc.of the IEEE 4th International Conference on Control Science and Systems Engineering, 2018: 499-504.
11	KENARI L R, ARVAN M R. Comparison of nearest neighbor and probabilistic data association methods for non-linear target tracking data association[C]//Proc.of the 2nd RSI/ISM International Conference on Robotics and Mechatronics, 2014: 47-52.
12	BARSHALOM Y , DAUM F , HUANG J . The probabilistic data association filter[J]. IEEE Control Systems, 2009, 29 (6): 82- 100. doi: 10.1109/MCS.2009.934469
13	MOAWAD R R, NOFAL H, DESSOUKY M I.Bearing only tracking in cluttered environment using seismic sensors[C]//Proc.of the 13th International Conference on Computer Engineering and Systems, 2018: 688-693.
14	HE S , SHIN H S , TSOURDOS A . Joint probabilistic data association filter with unknown detection probability and clutter rate[J]. Sensors, 2018, 18 (1): 269. doi: 10.3390/s18010269
15	ZHENG L , WANG X D . Improved NN-JPDAF for joint multiple target tracking and feature extraction[J]. Research Gate, 2017.
16	ZHENG L, ZHANG Y, WANG X.Feature-aided NN-JPDAF algorithm for multiple target tracking[C]//Proc.of the IEEE Radar Conference, 2019: 1-5.
17	CHEN C, JIE Z.Data association via logistic regression model for multiple target tracking problems[C]//Proc.of the 20th International Conference on Information Fusion, 2017: 1-6.
18	PAN S S, BAO Q, CHEN Z.An efficient TO-MHT algorithm for multi-target tracking in cluttered environment[C]//Proc.of the IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, 2017: 705-708.
19	SINHA A , DING Z , KIRUBARAJAN T , et al. Track quality based multi-target tracking approach for global nearest-neighbor association[J]. IEEE Trans.on Aerospace and Electronic Systems, 2012, 48 (2): 1179- 1191. doi: 10.1109/TAES.2012.6178056
20	SONG L C, YI Y B X, MING M Z.K-nearest neighbor joint possibility data association algorithm[C]//Proc.of the IEEE International Conference on Information Engineering and Computer Science, 2010: 1-4.
21	YUAN D B , MENG C Z , XU J , et al. Target association of heterogeneous sensors based on nearest-neighbor and topology[J]. Journal of Radars, 2012, 1 (4): 393- 398.
22	易哲, 王国宏, 张翔宇, 等. 一种高数据率雷达跟踪慢速目标的数据互联算法[J]. 电光与控制, 2014, 21 (7): 31- 35. doi: 10.3969/j.issn.1671-637X.2014.07.007
	YI Z , WANG G H , ZHANG X Y , et al. A data association algorithm for high sampling rate radar tracking slow moving target[J]. Electronics Optics and Control, 2014, 21 (7): 31- 35. doi: 10.3969/j.issn.1671-637X.2014.07.007
23	李晓峰, 王强. 改进的"最近邻"航迹相关算法研究[J]. 电子科技, 2008, 21 (7): 44- 46. doi: 10.3969/j.issn.1007-7820.2008.07.013
	LI X F , WANG Q . Research into improved nearest neighbor track correlation algorithm[J]. Electronic Science and Technology, 2008, 21 (7): 44- 46. doi: 10.3969/j.issn.1007-7820.2008.07.013
24	王晓君, 裴福俊, 刘红云. 一种改进马氏距离的最近邻数据关联算法[J]. 导航定位学报, 2015, 3 (4): 50- 56, 73.
	WANG X J , PEI F J , LIU H Y . Novel nearest neighbor data association algorithm based on improved mahalanobis distance[J]. Journal of Navigation and Positioning, 2015, 3 (4): 50- 56, 73.
25	葛建军, 李春霞. 一种基于信息熵的雷达动态自适应选择跟踪方法[J]. 雷达学报, 2017, 6 (6): 587- 593.
	GE J J , LI C X . A dynamic and adaptive selection radar tracking method based on information entropy[J]. Journal of Radars, 2017, 6 (6): 587- 593.
26	王树亮, 毕大平, 阮怀林, 等. 基于信息熵准则的认知雷达机动目标跟踪算法[J]. 电子学报, 2019, 47 (6): 1277- 1284. doi: 10.3969/j.issn.0372-2112.2019.06.014
	WANG S L , BI D P , RUAN H L , et al. Cognitive radar maneuvering target tracking algorithm based on information entropy criterion[J]. Acta Electronica Sinica, 2019, 47 (6): 1277- 1284. doi: 10.3969/j.issn.0372-2112.2019.06.014
27	周尚波, 蒋镇波, 谢显中, 等. 基于信息熵的Mean-shift目标跟踪[J]. 计算机系统应用, 2010, 19 (12): 49- 53. doi: 10.3969/j.issn.1003-3254.2010.12.010
	ZHOU S B , JIANG Z B , XIE X Z , et al. Object tracking using mean-shift based on comentropy[J]. Computer Systems Applications, 2010, 19 (12): 49- 53. doi: 10.3969/j.issn.1003-3254.2010.12.010
28	JIAN W.Target initiation of high frequency surface wave over-the-horizon radar based on information entropy theory[C]//Proc.of the IEEE CIE International Conference on Radar, 2011: 138-140.
29	LI C.A novel method to analyze accuracy of target measurement based on entropy[C]//Proc.of the IEEE 4th International Conference on Information Science and Control Engineering, 2017: 1364-1367.
30	赵恒, 苏永清, 叶萍. 快速传递对准滤波器设计及其计算复杂度分析[J]. 弹箭与制导学报, 2011, (3): 31- 34. doi: 10.3969/j.issn.1673-9728.2011.03.009
	ZHAO H , SU Y Q , YE P . Rapid transfer alignment filter design and computational complexity analysis[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2011, (3): 31- 34. doi: 10.3969/j.issn.1673-9728.2011.03.009
31	ZHAN X Y, WANG X L, LI L Q.Online multi-object tracking via maximum entropy intuitionistic fuzzy data association[C]//Proc.of the IEEE 14th International Conference on Signal Processing, 2018: 803-806.
32	KOVVALI N , BANAVAR M , SPANIAS A . An introduction to Kalman filtering with MATLAB examples[M]. Williston: Morgan & Claypool, 2013.
33	ZEKAVAT R , MICHAEL B R . An introduction to Kalman filtering implementation for localization and tracking applications[M]. Jersey: Wiley-IEEE Press, New, 2019: 143- 195.
34	CHEN Q X, YAN Y D, DAI Y W. Joint nearest neighbor data association based on interacting multiple model Kalman filtering[C]//Proc.of the IEEE 2nd International Conference on Computer and Communications, 2016: 75-79.

[1]	Shouzhen ZENG, Yingjie HU. Group decision making method for distributed linguistic trust network based on highly incomplete information [J]. Systems Engineering and Electronics, 2022, 44(6): 1907-1919.
[2]	Guang ZHAI, Yanxin WANG, Yiyong SUN. Cooperative tracking filtering technology of multi-target based on low orbit satellite constellation [J]. Systems Engineering and Electronics, 2022, 44(6): 1957-1967.
[3]	Xiao YI, Rui ZENG, Xinying CAO. Asynchronous anti-bias track association algorithm by using k-nearest neighbors interval distance [J]. Systems Engineering and Electronics, 2022, 44(5): 1475-1482.
[4]	Yingqi LU, Chengli FAN, Qiang FU, Xiaowen ZHU, Wei LI. Missile defense target threat assessment based on improved similarity measure and information entropy of IFRS [J]. Systems Engineering and Electronics, 2022, 44(4): 1230-1238.
[5]	Qi WANG, Zhizhong LIAO, Fei YAN. Algorithm for countering velocity gate pull-off jamming of radar seeker based on probability data association [J]. Systems Engineering and Electronics, 2022, 44(2): 448-454.
[6]	Xiao YI, Rui ZENG. Asynchronous track-to-track association algorithm based on k means distance of nearest neighbors [J]. Systems Engineering and Electronics, 2022, 44(11): 3515-3521.
[7]	Xinping MI, Xihong CHEN, Zan LIU, Yongjin LIU, Qiang LIU. Bispectrum feature recognition of radar signal based on entropy evaluation and modal decomposition [J]. Systems Engineering and Electronics, 2021, 43(8): 2116-2123.
[8]	Dongtao WEI, Xiaodong LIU, Peng LI, Yujin CHEN. Research on effectiveness evaluation method of equipment system based on node importance and improved information entropy [J]. Systems Engineering and Electronics, 2021, 43(12): 3614-3623.
[9]	Leilei ZHANG, Long XIE, Xu GAO, Tianyu SUN, Feng ZHANG. Research on synthetic reliability assessment method for high-value ammunition [J]. Systems Engineering and Electronics, 2021, 43(11): 3399-3404.
[10]	Zhenyu XIONG, Yaqi CUI, Wei XIONG, Xiangqi GU. Adaptive association for satellite and radar position data [J]. Systems Engineering and Electronics, 2021, 43(1): 91-98.
[11]	Zhenshuo LEI, Songtao LIU, Yang GE, Zhenming WEN. Parameter selection and feature representation method of jamming effect online evaluation [J]. Systems Engineering and Electronics, 2020, 42(12): 2755-2760.
[12]	Chen LI, Jun'an YANG, Hui LIU. Modulation recognition algorithm based on information entropy and GA-ELM [J]. Systems Engineering and Electronics, 2020, 42(1): 223-229.
[13]	LIANG Jialin, XIONG Wei. Capabilities assessment of the weaponry system based on combat ring [J]. Systems Engineering and Electronics, 2019, 41(8): 1810-1819.
[14]	ZHANG Liang, WANG Jianhao, Zheng Dongliang, CHE Fei, SHI Chao, MAO Hongbao. Equipment material supplier selection decision making based on intuitionistic fuzzy entropy and VIKOR [J]. Systems Engineering and Electronics, 2019, 41(7): 1568-1575.
[15]	XING Zhiwei, LI Biao, LUO Qian. Operational effectiveness evaluation of ground service for transit flight [J]. Systems Engineering and Electronics, 2019, 41(5): 1071-1078.

Nearest neighbor data association algorithm based on information entropy weight

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 34

Related Articles 15

Recommended Articles

Metrics

Comments