Reinforcement Learning-Based Multi-Target Detection Method for MIMO Radar Assisted by Strong Target Limitation

Under the background of co-located MIMO radar, the existing reinforcement learning (RL)-based multi-target detection methods generally perform poorly on weak targets. In our previous work, we have proposed a beam optimization scheme with strong target limitation and gave a solution approach based on multi-rank beamformer, to achieve focusing more radar transmit power on weak targets. In this letter, we further propose a solution approach based on inner convex approximation, which can achieve a higher power gain due to its improved freedom. In addition, we also design an approach for choosing the focused angle cells of radar by fusing the statistical prior information from previous time. Summarizing the above improvements, we propose a RL-based multi-target detection method for MIMO radar assisted by strong target limitation. The experiments show that our method owns better performance on weak targets than its competitors while maintaining the excellent performance on strong targets.

• Publication year

  2026

• Venue

  IEEE Signal Processing Letters

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/LSP.2025.3631431
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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