Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar
This work addresses clutter mitigation for radar systems in 6G networks, representing an incremental advancement in domain-specific sensing technology.
This paper tackled the problem of robust multitarget detection in dynamic environments for cognitive MIMO radar by integrating a robust Wald-type detector with a SARSA-based RL algorithm to adapt waveforms and beamforming, resulting in significant improvements in detection probability, especially for low SNR targets masked by clutter.
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.