DiffRIM: A Diffusion-Driven Model for High Efficiency Radar Interference Mitigation

Despite its all-weather superiority over optical sensors, the dense deployment of millimeter-wave radar in automotive applications causes severe vehicle-to-vehicle interference, degrading detection integrity and introducing safety risks. However, existing interference suppression methods are often limited by over-smoothing, we propose a Diffusion-based framework for real-time Radar Interference Mitigation (DiffRIM). Its stochastic forward process models the incremental addition of interference, and the learned reverse process conducts iterative removement, thereby significantly enhancing interpretability. We further introduce a Lightweight Autoencoder (LWAE) with Mobile Encoder and Decoder (ME/MD) modules, which extracts multi-scale spatial features through point-wise and hierarchical processing. A dual-residual (DualRes) connection mitigates gradient issues, while depthwise separable convolutions (DSC) and channel attention form an efficient spatio-temporal attention (STA) mechanism to extract sparse patterns. Extensive experimental evaluations demonstrate the superior mitigation and generalization performance of our approach in both synthetic and real-world datasets. To support the community, our implementation will be made publicly accessible on https://github.com/luluisthebest/DiffRim.