Volume 12 Issue 3
Jun.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(3): 629-641
SONG Xiaocheng, LI Zhi, REN Haiwei, et al. Threat-driven resource allocation algorithm for distributed netted phased array radars[J]. Journal of Radars, 2023, 12(3): 629–641. doi: 10.12000/JR22240
Citation: SONG Xiaocheng, LI Zhi, REN Haiwei, et al. Threat-driven resource allocation algorithm for distributed netted phased array radars[J]. Journal of Radars, 2023, 12(3): 629–641. doi: 10.12000/JR22240
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Threat-driven Resource Allocation Algorithm for Distributed Netted Phased Array Radars

doi: 10.12000/JR22240
  • 1.

    Beijing Institute of Electronic Engineering, Beijing 100854, China

  • 2.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (62231008, U19B2017), The Fundamental Research Funds for the Central Universities (ZYGX2020ZB029)
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  • Corresponding author: YI Wei, kussoyi@gmail.com
  • Received Date: 2022-12-22
  • Rev Recd Date: 2023-02-09
    • Available Online: 2023-02-11
  • Publish Date: 2023-02-22
    Abstract
  • For the Multi-Target Tracking (MTT) of distributed netted phased array radars, this paper proposes a joint beam and dwell time allocation algorithm driven by dynamic threats. First, a Bayesian Cramer-Rao Lower Bound (BCRLB), including beam and dwell time allocation, is derived. Then, a comprehensive threat evaluation scale is constructed based on the real-time motion state of the target, and a utility function based on the tracking accuracy reference threshold and contributed weights is designed for targets with different threats to measure the relationship of resource allocation prioritization among multiple targets. Afterward, an optimal distribution model of the joint beam and the dwell time driven by the dynamic threat of the target is established; the utility function is combined with the resources of the netted phased array radar system. Finally, the problem is solved using a reward-based iterative descent search algorithm, and the effectiveness of the algorithm is verified via simulation. The simulation results show that the proposed algorithm can determine the tracking accuracy requirements of different targets and allocate tracking resources based on the multi-target threat assessment results, thereby improving the comprehensive tracking accuracy of networked phased array radars.

     

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