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HU Yueyang, YI Jianxin, WAN Xianrong, et al. A track initiation method for FM-based passive radar network based on multiple elementary hypotheses[J]. Journal of Radars, in press. doi: 10.12000/JR23155
Citation: HU Yueyang, YI Jianxin, WAN Xianrong, et al. A track initiation method for FM-based passive radar network based on multiple elementary hypotheses[J]. Journal of Radars, in press. doi: 10.12000/JR23155

A Track Initiation Method for FM-Based Passive Radar Network Based on Multiple Elementary Hypotheses

doi: 10.12000/JR23155
Funds:  The National Natural Science Foundation of China (61931015, 62071335, 62250024), The Natural Science Foundation of Hubei Province of China (2021CFA002), The Fundamental Research Funds for the Central Universities of China (2042022dx0001)
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  • Corresponding author: YI Jianxin, jxyi@whu.edu.cn
  • Received Date: 2023-09-01
  • Rev Recd Date: 2023-10-30
  • Available Online: 2023-10-31
  • Passive radars based on FM radio signals have low detection probability, high false alarm rates and poor accuracy, presenting considerable challenges to target tracking in radar networks. Moreover, a high false alarm rate increases the computational burden and puts forward high requirements for the real-time performance of networking algorithms. In addition, low detection probability and poor azimuth accuracy result in a lack of redundant information, making measurement association and track initiation challenging. To address these issues, this paper proposes an FM-based passive radar network based on the concepts of elementary hypothesis points and elementary hypothesis track, as well as a track initiation algorithm. First, we construct possible low-dimensional association hypotheses and solve for their corresponding elementary hypothesis points. Subsequently, we associate elementary hypothesis points from different frames to form multiple possible elementary hypothesis tracks. Finally, by combining multi-frame radar network data for hypothesis track judgment, we confirm the elementary hypothesis tracks corresponding to the real targets, and eliminate the false elementary hypothesis tracks caused by incorrect associations. Result reveal that the proposed algorithm has lower computational complexity and faster track initiation speed than existing algorithms. Moreover, we verified the effectiveness of the proposed algorithm using simulation and experimental results.


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