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WU Xijie, LIU Tianpeng, LIU Yongxiang, et al. A multi-target detection method for distributed MIMO radar based on reinforcement learning[J]. Journal of Radars, in press. doi: 10.12000/JR25219
Citation: WU Xijie, LIU Tianpeng, LIU Yongxiang, et al. A multi-target detection method for distributed MIMO radar based on reinforcement learning[J]. Journal of Radars, in press. doi: 10.12000/JR25219
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A Multi-Target Detection Method for Distributed MIMO Radar Based on Reinforcement Learning

DOI: 10.12000/JR25219 CSTR: 32380.14.JR25219

  • School of Electronic Science, National University of Defense Technology, Changsha 410000, China

Funds:  The National Natural Science Foundation of China (62022091, 62201588)
More Information
  • Corresponding author: LIU Tianpeng, everliutianpeng@sina.cn
  • Received Date: 2025-10-31
    Available Online: 2026-01-24
    Abstract
  • Reinforcement learning (RL) is a critical approach for enabling cognitive radar target detection. Existing studies primarily focus on detection methods for centralized multiple-input multiple-output (MIMO) radar, which are limited to a single observation perspective. To address this issue, this paper proposes an RL-based multi-target detection method for a distributed MIMO radar system that possesses waveform and spatial diversity. The proposed method exploits spatial diversity to ensure robust target detection, while waveform diversity is used to construct a Markov decision process. Specifically, the radar first perceives target attributes through statistical signal detection techniques, then optimizes the transmit waveform accordingly, and iteratively updates its understanding of the environmental context using accumulated experience. This cyclic process gradually converges, yielding radar waveforms focused on target directions and achieving improved detection performance. To facilitate target localization, a maximization grid-based generalized likelihood ratio test detector for multi-antenna configurations is derived, using regularly shaped grids as the cell under test. For waveform optimization, two types of optimization problems, namely conventional and strong-target-limited formulations, are developed, and their solutions are obtained using continuous convex approximation. Simulation results across static and dynamic scenarios demonstrate that the proposed method can autonomously perceive environmental context and achieve superior detection performance compared with benchmark methods, particularly in weak target detection.

     

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