TY  - JOUR
AU  - Liu, Haiqi
AU  - Zhang, Xuqi
AU  - Zhou, Bin
AU  - Liu, Bing
AU  - Shen, Xiaojing
PY  - 2025
DA  - 2025/07/20
TI  - Distributed Group Target Tracking under Limited Field-of-View Sensors Using Belief Propagation
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 2
IS  - 3
SP  - 194
EP  - 211
DO  - 10.62762/CJIF.2025.314716
UR  - https://www.icck.org/article/abs/CJIF.2025.314716
KW  - group target tracking
KW  - distributed sensor network
KW  - consensus fusion
KW  - scalability
KW  - belief propagation
AB  - This paper considers the distributed group target tracking (DGTT) problem under sensors with limited and different field of views (FoVs). Usually, for the tracking of groups, targets within groups are closely spaced and move in a coordinated manner. These groups can split or merge, and the numbers of targets in groups may be large, which lead to more challenging issues related to data association, filtering and computational complexities. Particularly, these challenges may be further complicated in distributed fusion system architectures. To deal with these difficulties, we propose a consensus-based DGTT method within the belief propagation (BP) framework, which introduces undetected targets inside the FoV or new targets outside the FoV and performs the probabilistic track association via BP. Meanwhile, the obtained track association probabilities make it possible to exploit a probabilistic consensus fusion scheme for fusing local target densities. Furthermore, the proposed method exhibits computational scalability scaling only linearly on the numbers of group partitions, local measurements and neighboring sensors, and scaling quadratically on the number of targets. Numerical results validate the performance of the proposed method.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Liu2025Distribute,
  author = {Haiqi Liu and Xuqi Zhang and Bin Zhou and Bing Liu and Xiaojing Shen},
  title = {Distributed Group Target Tracking under Limited Field-of-View Sensors Using Belief Propagation},
  journal = {Chinese Journal of Information Fusion},
  year = {2025},
  volume = {2},
  number = {3},
  pages = {194-211},
  doi = {10.62762/CJIF.2025.314716},
  url = {https://www.icck.org/article/abs/CJIF.2025.314716},
  abstract = {This paper considers the distributed group target tracking (DGTT) problem under sensors with limited and different field of views (FoVs). Usually, for the tracking of groups, targets within groups are closely spaced and move in a coordinated manner. These groups can split or merge, and the numbers of targets in groups may be large, which lead to more challenging issues related to data association, filtering and computational complexities. Particularly, these challenges may be further complicated in distributed fusion system architectures. To deal with these difficulties, we propose a consensus-based DGTT method within the belief propagation (BP) framework, which introduces undetected targets inside the FoV or new targets outside the FoV and performs the probabilistic track association via BP. Meanwhile, the obtained track association probabilities make it possible to exploit a probabilistic consensus fusion scheme for fusing local target densities. Furthermore, the proposed method exhibits computational scalability scaling only linearly on the numbers of group partitions, local measurements and neighboring sensors, and scaling quadratically on the number of targets. Numerical results validate the performance of the proposed method.},
  keywords = {group target tracking, distributed sensor network, consensus fusion, scalability, belief propagation},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}