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A Track Splitting Determination Method for Elliptical Extended Targets Based on Spatio Temporal Similarity
Research Article | Published: 25 June 2025
Chinese Journal of Information Fusion Volume 2, Issue 2, 2025: 171-181
Volume 2, Issue 2, Chinese Journal of Information Fusion
Volume 2, Issue 2, 2025
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Jun Liu
Jun Liu
Hangzhou Dianzi University, China
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Chinese Journal of Information Fusion, Volume 2, Issue 2, 2025: 171-181

Open Access | Research Article | 25 June 2025
A Track Splitting Determination Method for Elliptical Extended Targets Based on Spatio Temporal Similarity
by
Jiashi Shen Jiashi Shen 1
Chaoqun Yang Chaoqun Yang 1,2 *
Lidong He Lidong He 3
Xianghui Cao Xianghui Cao 1
1 School of Automation, Southeast University, Nanjing 210096, China
2 Key Laboratory of Measurement and Control of Complex Systems of Engineering, Nanjing 210096, China
3 School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
* Corresponding Author: Chaoqun Yang, [email protected]
DOI: 10.62762/CJIF.2025.519610
Received: 27 March 2025, Accepted: 29 May 2025, Published: 25 June 2025  
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Abstract
Extended target tracking in occlusion scenarios often suffers from split errors due to sensor limitations and complex target interactions, leading to degraded tracking performance for autonomous vehicles and surveillance systems. To address this issue, in this paper, we propose a Gaussian Wasserstein distance-enhanced spatio-temporal similarity method for split error correction. We first analyze the spatio-temporal characteristics of split extended targets and model their geometric uncertainties via elliptical Gaussian distributions. Then, we integrate the Gaussian Wasserstein distance into the clue-aware trajectory similarity calculation framework to simultaneously capture positional and shape discrepancies, and designs an adaptive validation gate mechanism to dynamically adjust the threshold for track splitting, enabling accurate determination and fusion of split targets. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed method.
Graphical Abstract
A Track Splitting Determination Method for Elliptical Extended Targets Based on Spatio Temporal Similarity
Keywords
extended target tracking
target splitting
gaussian wasserstein distance
spatiotemporal trajectories
error correction
Data Availability Statement
Data will be made available on request.
Funding
This work was supported in part by the Jiangsu Province Natural Science Foundation of China under Grant BK20230827; in part by the National Natural Science Foundation of China under Grant 62303109; in part by the Zhishan Young Scholar Research Fund of Southeast University under Grant 2242024RCB0011; in part by the Southeast University Start-up Research Fund under Grant RF1028623002.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Ersü, C., Petlenkov, E., & Janson, K. (2024). A Systematic Review of Cutting-Edge Radar Technologies: Applications for Unmanned Ground Vehicles (UGVs). Sensors, 24(23), 7807.
    [CrossRef]   [Google Scholar]
  2. Wang, W., Xu, Y., Zhang, K., Sun, Y., & Li, P. (2025). A GGIW PMBM Smoother for Multiple Extended Object Tracking. Electronics Letters, 61(1), 1-5.
    [CrossRef]   [Google Scholar]
  3. Yang, C., Cao, X., & Shi, Z. (2023). Road-map aided Gaussian mixture labeled multi-Bernoulli filter for ground multi-target tracking. IEEE Transactions on Vehicular Technology, 72(6), 7137-7147.
    [CrossRef]   [Google Scholar]
  4. Granstrom, K., Lundquist, C., & Orguner, O. (2012). Extended target tracking using a Gaussian-mixture PHD filter. IEEE Transactions on Aerospace and Electronic Systems, 48(4), 3268-3286.
    [CrossRef]   [Google Scholar]
  5. Granstrom, K., & Orguner, U. (2012). A PHD filter for tracking multiple extended targets using random matrices. IEEE Transactions on Signal Processing, 60(11), 5657-5671.
    [CrossRef]   [Google Scholar]
  6. Habtemariam, B., Tharmarasa, R., Thayaparan, T., Mallick, M., & Kirubarajan, T. (2013). A multiple-detection joint probabilistic data association filter. IEEE Journal of Selected Topics in Signal Processing, 7(3), 461-471.
    [CrossRef]   [Google Scholar]
  7. Zhang, G., Lian, F., & Han, C. (2014). CBMeMBer filters for nonstandard targets, I: extended targets. In 17th International Conference on Information Fusion (pp. 1-6).
    [Google Scholar]
  8. Beard, M., Reuter, S., Granström, K., Vo, B. T., Vo, B. N., & Scheel, A. (2015). Multiple extended target tracking with labeled random finite sets. IEEE Transactions on Signal Processing, 64(7), 1638-1653.
    [CrossRef]   [Google Scholar]
  9. Granström, K., Fatemi, M., & Svensson, L. (2016). Gamma Gaussian inverse-Wishart Poisson multi-Bernoulli filter for extended target tracking. In 19th International Conference on Information Fusion (pp. 893-900).
    [Google Scholar]
  10. Xu, M., Yang, C., Cao, X., Yang, S., Cao, X., & Shi, Z. (2024). Irregular extended target tracking with unknown measurement noise covariance. Signal Processing, 225, 109600.
    [CrossRef]   [Google Scholar]
  11. Wang, G., Wang, Y., Gu, R., Hu, W., & Hwang, J. N. (2022). Split and connect: A universal tracklet booster for multi-object tracking. IEEE Transactions on Multimedia, 25, 1256-1268.
    [CrossRef]   [Google Scholar]
  12. Asad, M., Khan, S., Mehmood, Z., Shi, Y., Memon, S. A., & Khan, U. (2020). A split target detection and tracking algorithm for ballistic missile tracking during the re-entry phase. Defence Technology, 16(6), 1142-1150.
    [CrossRef]   [Google Scholar]
  13. Jin, J., Ma, M., & Liu, C. (2021, December). Group Targets Splitting Algorithm for Ballistic Missile Penetration Applications. In 2021 CIE International Conference on Radar (Radar) (pp. 843-847). IEEE.
    [CrossRef]   [Google Scholar]
  14. Dong, L., Zhang, Y., Zhao, L., Zheng, T., Wang, W., Li, J., ... & Yao, D. (2022). DRT: A new toolbox for the Standard EEG Data Structure in large-scale EEG applications. SoftwareX, 17, 100933.
    [CrossRef]   [Google Scholar]
  15. Yang, C., Liang, X., Shi, Z., Zhang, H., & Cao, X. (2025). Augmented LRFS-based filter: Holistic tracking of group objects. Signal Processing, 226, 1-10.
    [CrossRef]   [Google Scholar]
  16. Granström, K., Natale, A., Braca, P., Ludeno, G., & Serafino, F. (2015). Gamma Gaussian inverse Wishart probability hypothesis density for extended target tracking using X-band marine radar data. IEEE Transactions on Geoscience and Remote Sensing, 53(12), 6617-6631.
    [CrossRef]   [Google Scholar]
  17. Yang, S., Baum, M., & Granström, K. (2016). Metrics for performance evaluation of elliptic extended object tracking methods. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (pp. 523-528).
    [CrossRef]   [Google Scholar]
  18. Loraamm, R., Downs, J., Anderson, J., & Lamb, D. S. (2020). PySTPrism: Tools for voxel-based space–time prisms. SoftwareX, 12, 1-5.
    [CrossRef]   [Google Scholar]
  19. Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology, 6(3), 1-41.
    [CrossRef]   [Google Scholar]
  20. Sun, L., & Zhou, W. (2017). A multi-source trajectory correlation algorithm based on spatial-temporal similarity. In 20th International Conference on Information Fusion (pp. 1-7).
    [CrossRef]   [Google Scholar]
  21. Thormann, K., & Baum, M. (2019). Optimal fusion of elliptic extended target estimates based on the Wasserstein distance. In 22th International Conference on Information Fusion (pp. 1-6).
    [CrossRef]   [Google Scholar]
  22. Schuhmacher, D. , Vo, B. T. , & Vo, B. N.(2008). A consistent metric for performance evaluation of multi-object filters.IEEE Transactions on Signal Processing, 56(8), 3447-3457.
    [CrossRef]   [Google Scholar]
  23. Rahmathullah, A. S. , García-Fernández, Ángel F, & Svensson, L.(2016). Generalized optimal sub-pattern assignment metric. In 20th International Conference on Information Fusion (pp. 1-8).
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Shen, J., Yang, C., He, L., & Cao, X. (2025). A Track Splitting Determination Method for Elliptical Extended Targets Based on Spatio Temporal Similarity. Chinese Journal of Information Fusion, 2(2), 171–181. https://doi.org/10.62762/CJIF.2025.519610
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