Deep Unfolding Using Score-based Generative Networks for Automotive Radar Interference Mitigation

Abstract

Automotive frequency-modulated continuous wave (FMCW) radars, essential in Advanced Driver Assistance Systems, encounter mutual interference issues that degrade their detection capabilities. Model-based algorithms, though widely used, rely heavily on predetermined assumptions about the statistical properties. General-purpose black-box deep learning approaches, while effective in their training distribution, often lack flexibility and generalizability in dynamic environments. We introduce a novel hybrid method that combines model-based techniques with deep learning, treating interference mitigation as a source separation problem. Specifically, our method employs score-based deep generative networks to accurately capture the structure of FMCW interference. Additionally, we employ deep unfolding to accelerate inference, critical for automotive radar applications. Empirical results from simulated data demonstrate that the proposed algorithm outperforms the baseline models by 3.26 dB in signal-to-interference-plus-noise ratio in the presence of aggressive interference, and also shows good generalizability with measured data.