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YU Zhuang, LING Qing, YAN Wenjun, et al. Phased-array radar beam position partitioning for intercepted pulse sequences: an expert knowledge-based hybrid reinforcement learning framework[J]. Journal of Radars, in press. doi: 10.12000/JR25283
Citation: YU Zhuang, LING Qing, YAN Wenjun, et al. Phased-array radar beam position partitioning for intercepted pulse sequences: an expert knowledge-based hybrid reinforcement learning framework[J]. Journal of Radars, in press. doi: 10.12000/JR25283
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Phased-array Radar Beam Position Partitioning for Intercepted Pulse Sequences: An Expert Knowledge-based Hybrid Reinforcement Learning Framework

DOI: 10.12000/JR25283 CSTR: 32380.14.JR25283
  • 1.

    Institute of Information Fusion, Naval Aviation University, Yantai 264001, China

  • 2.

    Key Laboratory of Sea-Air information Perception and Processing Technology of Shandong Province, Yantai 264001, China

  • 3.

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

  • 4.

    Unit 92038, People’s Liberation Army, Qingdao 266071, China

Funds:  The National Natural Science Foundation of China (62371465), The Taishan Scholars Project Special Fund (ts201511020), Youth Innovation Teams in Shandong Province Fund (2022KJ084)
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    Abstract
  • The characteristics of phased-array radars—including flexible beam scanning, rapid multimode switching, and parameter agility—pose challenges to traditional radar signal analysis methods based on parameter clustering, causing feature parameter instability and parameter space overlap. To address these issues, this paper analyzes phased-array radar signals from the perspective of beam position partitioning. In particular, we reconstruct pulse subsequences corresponding to distinct beam positions from mixed pulse streams and an innovative expert-knowledge and hybrid-reinforcement-learning framework is proposed. This framework first performs preliminary partitioning using dynamic pulse amplitude thresholds. It subsequently feeds the preliminary results into a human-in-the-loop reinforcement–learning environment by integrating expert knowledge guidance with confidence assessment to ultimately achieve fine-grained beam position partitioning. Experimental results obtained using simulated datasets demonstrate that the proposed framework achieves a partitioning precision of 92.7%, indicating excellent calibration of the confidence assessment model. This work provides an effective technical pathway for human–machine collaboration in solving complex electromagnetic signal processing problems.

     

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