Volume 13 Issue 6
Dec.  2024
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ZHANG Xuqi, ZHOU Bin, LIU Haiqi, et al. A scalable method for group target tracking using multisensor with limited field of views[J]. Journal of Radars, 2024, 13(6): 1220–1238. doi: 10.12000/JR24054
Citation: ZHANG Xuqi, ZHOU Bin, LIU Haiqi, et al. A scalable method for group target tracking using multisensor with limited field of views[J]. Journal of Radars, 2024, 13(6): 1220–1238. doi: 10.12000/JR24054
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A Scalable Method for Group Target Tracking Using Multisensor with Limited Field of Views

DOI: 10.12000/JR24054
  • 1.

    Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China

  • 2.

    School of Mathematics, Sichuan University, Chengdu 610065, China

Funds:  The National Ministries Foundation
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  • Corresponding author: ZHANG Xuqi, 502671142@qq.com
  • Received Date: 2024-03-29
  • Rev Recd Date: 2024-05-25
    • Available Online: 2024-06-05
  • Publish Date: 2024-06-27
    Abstract
  • In practical applications, the field of view and computation resources of an individual sensor are limited, and the development and application of multisensor networks provide more possibilities for solving challenging target tracking problems. Compared with multitarget tracking, group target tracking encounters more challenging data association and computation problems due to factors such as the proximity of targets within groups, coordinated motions, a large number of involved targets, and group splitting and merging, which will be further complicated in the multisensor fusion systems. For group target trackingunder sensors with limited field of view, we propose a scalable multisensor group target tracking method via belief propagation. Within the Bayesian framework, the method considers the uncertainty of the group structure, constructs the decomposition of the joint posterior probability density of the multisensor group targets and corresponding factor graph, and efficiently solves the data association problem by running belief propagation on the devised factor graph. Furthermore, the method has excellent scalability and low computational complexity, scaling linearly only on the numbers of sensors, preserved group partitions, and sensor measurements, and scaling quadratically on the number of targets. Finally, simulation experiments compare the performance of different methods on GOSPA and OSPA(2), which verify that the proposed method can seamlessly track grouped and ungrouped targets, fully utilize the complementary information among sensors, and improve tracking accuracy.

     

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