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

arXiv: https://arxiv.org/abs/2502.04967 · HTML: https://mlsanity.com/paper/2502.04967/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental findings and applications](https://mlsanity.com/paper/1709.08210/) (2017)
- [Massive MIMO Radar for Target Detection](https://mlsanity.com/paper/1906.06191/) (2019)

## Built on by (most cited first, 1 of 1)

- [RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs](https://mlsanity.com/paper/2511.02672/) (2025)

## Abstract (arXiv)

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.