Adaptive FMCW Radar Interference Suppression via Coupled Range-Doppler Unsupervised Sparse Learning and Reconstruction

This is the authors' abstract. We don't add key points for this paper.

The widespread adoption of frequency-modulated continuous wave (FMCW) radar operating in microwave bands has made mutual interference inevitable. Currently, conventional hardware-based solutions increase microwave system complexity, while microwave signal-processing methods struggle with effective interference suppression under limited samples. To address these challenges, this article proposes a range-Doppler coupled learning and reconstruction network (RDLR-Net) for adaptive suppression of interference between FMCW radars. First, the interference suppression is reformulated as a microwave signal reconstruction problem with a coupled range-Doppler optimization. Subsequently, a novel regularization constraint model is introduced, which combines the <tex-math notation="LaTeX">l₁</tex-math> norm and target capture operator to effectively separate strong and weak scattering targets. A framework based on distributed optimization is designed and unfolded into a deep feedforward network to accelerate model training and inference. RDLR-Net adopts an unsupervised learning strategy to invert the target signal onto the original echo with interference, thus completing the loss term mapping. The simulation and multiple measured experiments demonstrate the proposed method achieves efficient interference suppression performance. Compared to state-of-the-art methods, the time required to process a single frame of measured data is only 0.0872 s, with the best interference suppression performance.