Model-Based Diffusion for Mitigating Automotive Radar Interference

Mitigating automotive radar-to-radar interference is a challenging task, especially when the observed signal is densely corrupted with highly correlated interference signals. In this paper, we propose to remove this interference using joint-conditional posterior sampling with score-based diffusion models. These models use three individual scores: a target score, an interference score, and a joint data consistency score. Leveraging the sparsity of clean target signals in the Fourier domain, we propose a model-based score estimator for the target signals, derived from the proximal step of the ℓ₁-norm. For the interference score, we use a neural network with denoising score-matching, given that it is difficult to obtain analytical statistical models of the interference signals. Lastly, the target and interference scores are connected by a data-consistency score. Experimental results show that our solution results in superior performance over state-of-the-art methods, in terms of normalized mean squared error (NMSE) and receiver operating characteristic (ROC) curves.