Track fusion algorithm based on improved FCM and information entropy correction

ZHEN Xu; LIU Fang

doi:10.7527/S1000-6893.2021.25236

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2022 , Vol. 43 >Issue 5: 325236 - 325236

DOI: https://doi.org/10.7527/S1000-6893.2021.25236

Electronics and Electrical Engineering and Control

Track fusion algorithm based on improved FCM and information entropy correction

ZHEN Xu ,
LIU Fang

Expand

National Key Laboratory of Science and Technology on Automatic Target Recognition, National Defense Science and Technology University, Changsha 410005, China

Received date: 2021-01-08

Revised date: 2021-02-06

Online published: 2021-02-24

Fold

Abstract

When the quality of local track information is not balanced, the algorithm of choosing all local tracks for track fusion will lead to degradation of track quality. To improve tracking performance, a track fusion algorithm is proposed based on the improved Fuzzy C-Means (FCM) and information entropy correction. The "quality" of the clustering data is modified by the track information filtered with the Interactive Multi-mModel (IMM). The improved FCM algorithm is used to cluster the local track. The local tracks are selected and fused by using information entropy and membership degree to modify the clustering center and improve the quality of the system track. Simulation results show that when multiple sensors are used to track maneuvering targets, the tracking performance of the proposed algorithm is better than the known track fusion algorithms in the case of changing of sensor observation accuracy and measurement loss.

Key words： incomplete information; information entropy; membership; track selection; track fusion; fuzzy clustering

Cite this article

ZHEN Xu , LIU Fang . Track fusion algorithm based on improved FCM and information entropy correction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022 , 43(5) : 325236 -325236 . DOI: 10.7527/S1000-6893.2021.25236

References

[1] 胡振涛, 刘先省. 基于动态加权的分布式多传感器航迹融合算法[J]. 计算机应用研究, 2006, 23(6):59-61. HU Z T, LIU X X. Distributed multi-sensor track fusion algorithm based on dynamic weight[J]. Application Research of Computers, 2006, 23(6):59-61(in Chinese).
[2] SINGER R A. Estimating optimal tracking filter performance for manned maneuvering targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 1970, AES-6(4):473-483.
[3] 从金亮,李银伢,戚国庆. 快速协方差交叉融合算法及应用[J]. 自动化学报,2020,46(7):1433-1444. CONG J L,LI Y Y, QI G Q.Fast covariance cross fusion algorithm and its application[J].Journal of Automation,2020,46(7):1433-1444(in Chinese).
[4] 陈科文, 张祖平, 龙军. 多源信息融合关键问题、研究进展与新动向[J]. 计算机科学, 2013, 40(8):6-13. CHEN K W, ZHANG Z P, LONG J. Multisource information fusion:Key issues, research progress and new trends[J]. Computer Science, 2013, 40(8):6-13(in Chinese).
[5] MA J, SUN S L. Linear estimators for networked systems with one-step random delay and multiple packet dropouts based on prediction compensation[J]. IET Signal Processing, 2017, 11(2):197-204.
[6] YANG C, ZHENG J Y, REN X Q, et al. Multi-sensor Kalman filtering with intermittent measurements[J]. IEEE Transactions on Automatic Control, 2018, 63(3):797-804.
[7] 许志刚, 盛安冬, 郭治. 基于不完全量测下离散线性滤波的修正Riccati方程[J]. 控制理论与应用, 2009, 26(6):673-677. XU Z G, SHENG A D, GUO Z. The modified Riccati equation for discrete-time linear filtering with incomplete measurements[J]. Control Theory & Applications, 2009, 26(6):673-677(in Chinese).
[8] 吴黎明, 马静, 孙书利. 具有不同观测丢失率多传感器随机不确定系统的加权观测融合估计[J]. 控制理论与应用, 2014, 31(2):244-249. WU L M, MA J, SUN S L. Weighted measurement fusion estimation for stochastic uncertain systems with multiple sensors of different missing measurement rates[J]. Control Theory & Applications, 2014, 31(2):244-249(in Chinese).
[9] 楚天鹏. 不完全信息下分布式目标跟踪算法[J]. 兵工自动化, 2017, 36(9):39-44. CHU T P. Distributed target tracking algorithm with incomplete information[J]. Ordnance Industry Automation, 2017, 36(9):39-44(in Chinese).
[10] 雷延花, 张聃, 蔡云泽, 等. 基于IMM的不完全观测下目标跟踪算法研究[J]. 上海航天, 2016, 33(1):6-9, 74. LEI Y H, ZHANG D, CAI Y Z, et al. Algorithms for target tracking of incomplete observation based on IMM[J]. Aerospace Shanghai, 2016, 33(1):6-9, 74(in Chinese).
[11] DING J, SUN S L, MA J, et al. Fusion estimation for multi-sensor networked systems with packet loss compensation[J]. Information Fusion, 2019, 45:138-149.
[12] CHEN B, ZHANG W A, YU L. Distributed fusion estimation with missing measurements, random transmission delays and packet dropouts[J]. IEEE Transactions on Automatic Control, 2014, 59(7):1961-1967.
[13] 李妍妍, 张伟, 陈明燕. 一种基于航迹隶属度的动态加权融合算法[J]. 计算机应用研究, 2013, 30(5):1334-1336, 1369. LI Y Y, ZHANG W, CHEN M Y. Kind of dynamic weigh fusion algorithm based on track fuzzy membership[J]. Application Research of Computers, 2013, 30(5):1334-1336, 1369(in Chinese).
[14] 冉金和, 张玉. 基于航迹隶属度的分布式系统数据融合算法[J]. 信号处理, 2011, 27(2):226-229. RAN J H, ZHANG Y. Distributed system data fusion algorithm based on track fuzzy membership[J]. Signal Processing, 2011, 27(2):226-229(in Chinese).
[15] 陈帅, 张世仓, 王凯. 基于跟踪质量熵的分布式组网雷达航迹融合算法[J]. 电光与控制, 2019, 26(5):39-44. CHEN S, ZHANG S C, WANG K. A track fusion algorithm of distributed netted radar based on track quality entropy[J]. Electronics Optics & Control, 2019, 26(5):39-44(in Chinese).
[16] ZHANG K, WANG Z Y, GUO L L, et al. An asynchronous data fusion algorithm for target detection based on multi-sensor networks[J]. IEEE Access, 2020, 8:59511-59523.
[17] 徐丽, 马培军, 苏小红. 基于不确定性分析的多传感器航迹融合算法[J]. 宇航学报, 2011, 32(3):567-573. XU L, MA P J, SU X H. An uncertainty analysis-based algorithm track fusion[J]. Journal of Astronautics, 2011, 32(3):567-573(in Chinese).
[18] XU L, MA P J, SU X H. Selective track fusion[C]//2011 International Conference on Neural Information Processing, 2011:18-25.
[19] 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(in Chinese).
[20] 葛建良, 葛洪伟, 王冬, 等. 一种结合交互多模型的多机动扩展目标跟踪算法[J]. 小型微型计算机系统, 2018, 39(2):334-339. GE J L, GE H W, WANG D, et al. Multiple maneuvering extended target tracking algorithm based on interacting multiple model[J]. Journal of Chinese Computer Systems, 2018, 39(2):334-339(in Chinese).
[21] 朱洪峰, 熊伟, 崔亚奇, 等. 新的自适应转弯模型的IMM算法研究[J]. 计算机工程与应用, 2019, 55(17):252-258. ZHU H F, XIONG W, CUI Y Q, et al. Research on IMM algorithm of new adaptive turn model[J]. Computer Engineering and Applications, 2019, 55(17):252-258(in Chinese).
[22] 邢婷, 邢治国, 王凤领. 基于信息熵的FCM聚类算法[J]. 计算机工程与设计, 2010, 31(23):5092-5095. XING T, XING Z G, WANG F L. FCM clustering algorithm based on information entropy[J]. Computer Engineering and Design, 2010, 31(23):5092-5095(in Chinese).
[23] 苏璇, 王晓晔, 王卓. 基于信息熵的模糊聚类新算法研究[J]. 天津理工大学学报, 2010, 26(5):57-60. SU X, WANG X Y, WANG Z. New fuzzy clustering algorithm based on information entropy[J]. Journal of Tianjin University of Technology, 2010, 26(5):57-60(in Chinese).
[24] 于剑. 论模糊C均值算法的模糊指标[J]. 计算机学报, 2003, 26(8):968-973. YU J. On the fuzziness index of the FCM algorithms[J]. Chinese Journal of Computers, 2003, 26(8):968-973(in Chinese).
[25] 诸克军, 苏顺华, 黎金玲. 模糊C-均值中的最优聚类与最佳聚类数[J]. 系统工程理论与实践, 2005, 25(3):52-61. ZHU K J, SU S H, LI J L. Optimal number of clusters and the best partition in fuzzy C-mean[J]. Systems Engeering-Theory & Practice, 2005, 25(3):52-61(in Chinese).

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References