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ZHAO Yingjian, JIANG Libing, ZHENG Shuyu, et al. Adaptive phd-bof: a slow-moving targets tracking method with air surveillance radar[J]. Journal of Radars, in press. doi: 10.12000/JR25081
Citation: ZHAO Yingjian, JIANG Libing, ZHENG Shuyu, et al. Adaptive phd-bof: a slow-moving targets tracking method with air surveillance radar[J]. Journal of Radars, in press. doi: 10.12000/JR25081
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Adaptive PHD-BOF: A Slow-Moving Targets Tracking Method with Air Surveillance Radar

DOI: 10.12000/JR25081 CSTR: 32380.14.JR25081
  • 1.

    National Key Laboratory of Automatic Target Recognition, National University of Defense Technology, Changsha 410073, China

  • 2.

    Laboratory of Intelligent Control, PLA Rocket Force University of Engineering, Xi’an 710025, China

Funds:  The Key Library Foundation of China (JKWATR-230301),
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  • Corresponding author: WANG Zhuang, zhuang_wang@sina.com
  • Received Date: 2025-04-29
    Available Online: 2025-09-11
    Abstract
  • Low-altitude targets, represented by rotor unmanned aerial vehicles, can typically adopt a slow-cruise mode. As a result, their echoes fall within the Doppler blind zone (DBZ) and evade radar detection and tracking. The cluttered low-altitude environment adds to further complexity. To address this issue, this study proposes a method grounded in the framework of random finite set and designed for tracking slow-moving targets with a low-altitude surveillance radar. Inspired by the Bayesian occupancy filter, the proposed method initially models the radar field of view (FoV) as a grid map. It is uniformly partitioned along the angle-range axis, ensuring that each cell captures a specific segment of the FoV. Then, adaptive filtering parameter modules are meticulously designed by leveraging the distinct dynamic characteristics of slow-moving targets and ground clutter. Subsequently, a probability hypothesis density filter is deployed to conduct unified filtering on the grid map situated within the DBZ. The final step involves the use of clustering methods to extract information about the target of interest. Simulation results validate the effectiveness, robustness, and superior performance of the proposed method across typical surveillance scenarios involving multiple slow-moving targets, noise, and clutter.

     

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