TY - GEN
T1 - Research on Multi-high-speed and High-Maneuverability Target Tracking Method Based on GM-PHD Filter
AU - Wang, Chenxin
AU - Lin, Fanyong
AU - Zhao, Na
AU - Geng, Haifeng
AU - Yang, Guangyu
AU - Fu, Wenxing
N1 - Publisher Copyright:
© Beijing HIWING Scientific and Technological Information Institute 2026.
PY - 2026
Y1 - 2026
N2 - For the problem of multi-target positioning and tracking of high-speed and highly maneuverable targets, a Probability Hypothesis Density (PHD) filter algorithm, which can simultaneously estimate the number of targets and their positions, is commonly used. Among PHD filter algorithms, the Generalized Multi-Object Probability Hypothesis Density (GM-PHD) filter is the most widely adopted due to its computational simplicity. However, the currently used GM-PHD filter has its limitations, and when applied to long-distance positioning of high-speed and highly maneuverable targets, the original GM-PHD filter algorithm may introduce new errors. To address this issue, this paper proposes improvements to the GM-PHD filter algorithm by eliminating the merging step, making it more suitable for the positioning and tracking of high-speed and highly maneuverable targets. Simulation results demonstrate that the improved filtering algorithm reduces the tracking error to 96% of the original error.
AB - For the problem of multi-target positioning and tracking of high-speed and highly maneuverable targets, a Probability Hypothesis Density (PHD) filter algorithm, which can simultaneously estimate the number of targets and their positions, is commonly used. Among PHD filter algorithms, the Generalized Multi-Object Probability Hypothesis Density (GM-PHD) filter is the most widely adopted due to its computational simplicity. However, the currently used GM-PHD filter has its limitations, and when applied to long-distance positioning of high-speed and highly maneuverable targets, the original GM-PHD filter algorithm may introduce new errors. To address this issue, this paper proposes improvements to the GM-PHD filter algorithm by eliminating the merging step, making it more suitable for the positioning and tracking of high-speed and highly maneuverable targets. Simulation results demonstrate that the improved filtering algorithm reduces the tracking error to 96% of the original error.
KW - GM-PHD filter
KW - high-speed and high-maneuverability
KW - long-range positioning
KW - multi-target tracking
UR - https://www.scopus.com/pages/publications/105042909931
U2 - 10.1007/978-981-95-7660-9_1
DO - 10.1007/978-981-95-7660-9_1
M3 - 会议稿件
AN - SCOPUS:105042909931
SN - 9789819576593
T3 - Lecture Notes in Electrical Engineering
SP - 1
EP - 13
BT - Proceedings of 5th 2025 International Conference on Autonomous Unmanned Systems, ICAUS - Volume 7
A2 - Xie, Shaorong
A2 - Niu, Yifeng
A2 - Fu, Wenxing
A2 - Qu, Yi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Autonomous Unmanned Systems, ICAUS 2025
Y2 - 17 October 2025 through 19 October 2025
ER -