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JIAO Hao, YAN Junkun, HAO Jia, et al. Resource allocation and precise tracking algorithm for multiple maneuvering targets[J]. Journal of Radars, in press. doi: 10.12000/JR25037
Citation: JIAO Hao, YAN Junkun, HAO Jia, et al. Resource allocation and precise tracking algorithm for multiple maneuvering targets[J]. Journal of Radars, in press. doi: 10.12000/JR25037
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Resource Allocation and Precise Tracking Algorithm for Multiple Maneuvering Targets

DOI: 10.12000/JR25037 CSTR: 32380.14.JR25037
  • 1.

    National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

  • 2.

    Academy of Air Force, Beijing 100085, China

Funds:  The National Natural Science Foundation of China (62471356, 62101350, 62192714), The Industry-University Research Cooperation of the and Technology 8th Research Institute of China Aerospace Science Corporation (SAST2023-068), The Natural Science Basic Research Program of Shaanxi (2025JC-QYCX-058)
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  • Corresponding author: YAN Junkun, jkyan@xidian.edu.cn
  • Received Date: 2025-02-24
  • Rev Recd Date: 2025-06-08
    • Available Online: 2025-06-25
    Abstract
  • In radar systems that track multiple maneuvering targets, conventional approaches often suffer from performance degradation due to suboptimal resource allocation and insufficient utilization of prior information. To address this challenge and significantly enhance tracking performance under equivalent resource constraints, a resource allocation and precise tracking algorithm for multiple maneuvering targets is proposed. First, by integrating a multiple model interaction architecture with tracker feedback prediction, a probabilistic distribution model for target position prediction through multiple model interaction is constructed. This model establishes an integrated detection and tracking method based on multiple model interactions to achieve precise tracking of maneuvering targets. Subsequently, by analytically modeling the coupling mechanism between radar resources and tracking performance, and deriving the Bayesian Cramér-Rao Lower Bound (BCRLB) for maneuvering targets, a performance-driven multimodel weighted resource allocation framework is developed. Simulations validate that the proposed method can significantly enhance the overall tracking precision of multiple maneuvering targets under equivalent resource consumption.

     

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