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http://dx.doi.org/10.3837/tiis.2021.07.003

A Novel Algorithm of Joint Probability Data Association Based on Loss Function  

Jiao, Hao (School of Photo-Electronic Information Science and Technology,Yantai University)
Liu, Yunxue (School of Photo-Electronic Information Science and Technology,Yantai University)
Yu, Hui (Yantai Vocational College)
Li, Ke (Xi'an Wangsi Xunda Information Technology Co. LTD)
Long, Feiyuan (Wolfson school of Mechanical, Electrical & Manufacturing Engineering, Loughborough University)
Cui, Yingjie (School of Photo-Electronic Information Science and Technology,Yantai University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.7, 2021 , pp. 2339-2355 More about this Journal
Abstract
In this paper, a joint probabilistic data association algorithm based on loss function (LJPDA) is proposed so that the computation load and accuracy of the multi-target tracking algorithm can be guaranteed simultaneously. Firstly, data association is divided in to three cases based on the relationship among validation gates and the number of measurements in the overlapping area for validation gates. Also the contribution coefficient is employed for evaluating the contribution of a measurement to a target, and the loss function, which reflects the cost of the new proposed data association algorithm, is defined. Moreover, the equation set of optimal contribution coefficient is given by minimizing the loss function, and the optimal contribution coefficient can be attained by using the Newton-Raphson method. In this way, the weighted value of each target can be achieved, and the data association among measurements and tracks can be realized. Finally, we compare performances of LJPDA proposed and joint probabilistic data association (JPDA) algorithm via numerical simulations, and much attention is paid on real-time performance and estimation error. Theoretical analysis and experimental results reveal that the LJPDA algorithm proposed exhibits small estimation error and low computation complexity.
Keywords
Multi-target tracking; Data association; Loss function; Joint probability; Association probability;
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1 M. Beard, B. Vo and B. Vo, "Bayesian Multi-Target Tracking with Merged Measurements Using Labelled Random Finite Sets," IEEE Transactions on Signal Processing, vol. 63, no. 6, pp. 1433-1447, March15, 2015.   DOI
2 T. Wang, X. Wang, Z. Zhao, Z. He and T. Xia, "Measurement Data Classification Optimization Based on a Novel Evolutionary Kernel Clustering Algorithm for Multi-Target Tracking," IEEE Sensors Journal, vol. 18, no. 9, pp. 3722-3733, 1 May1, 2018.   DOI
3 D. Jwo, "Complementary Kalman filter as a baseline vector estimator for GPS-based attitude determination," Computers, Materials & Continua, vol. 65, no. 2, pp. 993-1014, 2020.   DOI
4 H. H. Sonmez, K. Turgut and A. K. Hocaoglu, "Improving track initiation performance by track validation algorithms for multi-target tracking in heavy clutter," in Proc. of 25th Signal Processing and Communications Applications Conference (SIU), Antalya, pp. 1-4, 2017.
5 Yang Wanhai, Multisensor data fusion with applications, Xian, China: Xidian University Publishing House, 2004
6 Y.Bar-Shalom,T.E and Fortmann, Tracking and Data Association, Pittsburgh, PA, USA: Academic Press,1988.
7 Y. Lee, H. Ahn, H. Ahn and S. Lee, "Visual object detection and tracking using analytical learning approach of validity level," Intelligent Automation & Soft Computing, vol. 25, no.1, pp. 205-215, 2019.
8 A. Qayyum, I. Ahmad, M. Iftikhar and M. Mazher, "Object detection and fuzzy-based classification using uav data," Intelligent Automation & Soft Computing, vol. 26, no.4, pp. 693-702, 2020.   DOI
9 Hu ChangLin, Guo Bo, "An Algorithm of Track Probability Data Association Based on Bayes," Modern Radar, vol. 40, no. 5, pp. 41-45, 2018.
10 S. He, H. Shin and A. Tsourdos, "Multi-Sensor Multi-Target Tracking Using Domain Knowledge and Clustering," IEEE Sensors Journal, vol. 18, no. 19, pp. 8074-8084, 1 Oct.1, 2018.   DOI
11 Singer, R, and Sea, "New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments," Automatic Control IEEE Transactions on, vol. 18, no. 6, pp. 571-582, 1973.   DOI
12 Reid, D. B, "An algorithm for tracking multiple targets," IEEE Transactions on Automatic Control, vol. 24, no. 6, pp. 843-854, 1979.   DOI
13 Fortmann T, Y. Bar-Shalom, and M. Scheffe, "Sonar tracking of multiple targets using joint probabilistic data association," IEEE Journal of Oceanic Engineering, vol. 8, no. 3, pp. 173-184, 1983.   DOI
14 G. Wang, C. Feng, J. Tao, R. Mo and M. Zhang, "Research on multi-maneuvering target tracking JPDA algorithm," in Proc. of the Chinese Control and Decision Conference (CCDC), Shenyang, pp. 3558-3561, 2018.
15 Ding, Z, and H. Leung, "Decoupling joint probabilistic data association algorithm for multiple target tracking," Iee Proceedings Radar Sonar & Navigation, vol. 146, no. 5, pp. 251-254, 1999.   DOI
16 HE You, Xiu JianJuan, Zhang JingWei, Guan Xin, et al., Radar Data Processing with Applications, Beijing, China: Publishing House of Electronics Industry, 2009
17 C. L. Li, X. M. Sun and J. H. Cai, "Intelligent mobile drone system based on real-time object detection," Journal on Artificial Intelligence, vol. 1, no. 1, pp. 1-8, 2019.   DOI
18 J. Liu, B. Zhang, X. Cheng, Y. Chen and L. Zhao, "An adaptive superpixel tracker using multiple features," Computers, Materials & Continua, vol. 60, no. 3, pp. 1097-1108, 2019.   DOI
19 Li QingYang, Wang NengChao, Yi DaYi, Numerical Analysis, Beijing, China: Tsinghua University Press, 2008.
20 N. Amrouche, A. Khenchaf and D. Berkani, "Multiple target tracking using track before detect algorithm," in Proc. of the International Conference on Electromagnetics in Advanced Applications (ICEAA), Verona, pp. 692-695, 2017.
21 Yoshio, et al, "A track-oriented multiple hypothesis multitarget tracking algorithm," Electronics & Communications in Japan, vol. 82, no. 12, pp. 84-92, 1999.   DOI
22 Z. Shi and X. Xu, "Near and Supersonic Target Tracking Algorithm Based on Adaptive Kalman Filter," in Proc. of 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, pp. 418-421, 2016.
23 Song, Taek Lyul, and D. G. Lee, "A probabilistic nearest neighbor filter algorithm form validated measurements," IEEE Transactions on Signal Processing, vol. 54, no. 7, pp. 2797-2802, 2006.   DOI