Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar

TL;DR

This paper introduces a cognitive MIMO radar system that uses reinforcement learning to adaptively mitigate 2D clutter, significantly improving multitarget detection in complex environments for future 6G networks.

Contribution

It presents a novel RL-based adaptive waveform and beamforming strategy for 2D clutter mitigation in MIMO radar, enhancing detection robustness in dynamic scenarios.

Findings

Improved detection probability in cluttered environments.

Effective adaptation to unknown 2D disturbances.

Enhanced performance at low SNR levels.

Abstract

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.