用强化学习让雷达自适应抗二维杂波，提升低信噪比目标探测能力。
Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar
- 基于SARSA强化学习动态优化波形与波束，应对复杂二维杂波环境。
- 在低信噪比下检测概率显著优于全向探测方法。
- 适合研究6G智能感知或雷达自适应系统的研究者参考。
针对6G网络中感知与通信一体化的迫切需求，本文提出一种由强化学习驱动的认知多输入多输出（MIMO）雷达系统，旨在动态环境下实现鲁棒的多目标检测。该系统采用平面阵列结构，通过自适应调整发射波形和波束成形策略，以优化检测性能，应对未知的二维（2D）干扰。系统融合鲁棒Wald型检测器与基于SARSA的强化学习算法，使雷达能够学习并适应由二维自回归过程建模的复杂杂波环境。仿真结果表明，在低信噪比（SNR）条件下，目标被杂波掩盖时，该方法相比全向探测方式显著提升了检测概率。
原文摘要 · Abstract (English)
Motivated by the growing interest in integrated sensing and communication for 6th generation (6G) networks, this paper presents a cognitive Multiple-Input Multiple-Output (MIMO) radar system enhanced by reinforcement learning (RL) for robust multitarget detection in dynamic environments. The system employs a planar array configuration and adapts its transmitted waveforms and beamforming patterns to optimize detection performance in the presence of unknown two-dimensional (2D) disturbances. A robust Wald-type detector is integrated with a SARSA-based RL algorithm, enabling the radar to learn and adapt to complex clutter environments modeled by a 2D autoregressive process. Simulation results demonstrate significant improvements in detection probability compared to omnidirectional methods, particularly for low Signal-to-Noise Ratio (SNR) targets masked by clutter.
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