Volume 12 Issue 2
Apr.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(2): 412-424
WANG Yingjie, WANG Robert, YU Weidong, et al. See-Earth: SAR constellation with dense time-series for multi-dimensional environmental monitoring of the earth[J]. Journal of Radars, 2021, 10(6): 842–864. doi: 10.12000/JR21176
Citation: ZHU Peikun, LIANG Jing, LUO Zihan, et al. Waveform selection method of cognitive radar target tracking based on reinforcement learning[J]. Journal of Radars, 2023, 12(2): 412–424. doi: 10.12000/JR22239
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Waveform Selection Method of Cognitive Radar Target Tracking Based on Reinforcement Learning

DOI: 10.12000/JR22239

  • 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 (61731006), Sichuan Natural Science Foundation (2023NSFSC0450), The 111 Project under Grant (B17008)
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  • Corresponding author: LIANG Jing, liangjing@uestc.edu.cn
  • Received Date: 2022-12-21
  • Rev Recd Date: 2023-02-08
    • Available Online: 2023-02-11
  • Publish Date: 2023-02-22
    Abstract
  • Based on the obtained knowledge through ceaseless interaction with the environment and learning from the experience, cognitive radar continuously adjusts its waveform, parameters, and illumination strategies to achieve robust target tracking in complex and changing scenarios. Its waveform design has been receiving attention to improve tracking performance. In this paper, we propose a novel framework of cognitive radar waveform selection for the tracking of high-maneuvering targets. The framework considers the combination of Constant Velocity (CV), Constant Acceleration (CA), and Coordinate Turn (CT) motions. We also design Criterion-Based Optimization (CBO) and Entropy Reward Q-Learning (ERQL) methods to perform waveform selection based on this framework. To provide the optimum target tracking performance, it merges the radar and target into a closed loop, updating the broadcast waveform in real-time as the target state changes. The suggested ERQL technique achieves about the same tracking performance as the CBO while using much less processing time than the CBO, according to numerical results. The proposed ERQL method significantly increases the tracking accuracy of moving targets as compared to the fixed parameter approach.

     

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