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

• URL: http://arxiv.org/abs/2502.04967v1
• Date: Fri, 07 Feb 2025 14:31:58 GMT
• Title: Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar
• Authors: Adam Umra, Aya Mostafa Ahmed, Aydin Sezgin,
• Abstract summary: The system employs a planar array configuration and adapts its transmitted waveforms and beamforming patterns to optimize detection performance. A robust Wald-type detector is integrated with a SARSA-based RL algorithm, enabling the radar to learn and adapt to complex clutter environments.
• Score: 8.674241138986925
• License:
• 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.

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