Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909482927259648 |
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| author | Umra, Adam Ahmed, Aya Mostafa Sezgin, Aydin |
| author_facet | Umra, Adam Ahmed, Aya Mostafa Sezgin, Aydin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04967 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar Umra, Adam Ahmed, Aya Mostafa Sezgin, Aydin Signal Processing Machine Learning 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. |
| title | Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2502.04967 |