Score-based Generative Modeling for Interference Mitigation in Automotive FMCW Radar

Automotive radar interference is a growing problem as automotive radars proliferate in advanced driver assistance systems and autonomous driving. Numerous studies have been proposed to address interference mitigation based on hand-crafted priors, like sparsity-based techniques, or through purely data-driven approaches. However, their effectiveness is often compromised when these representations fail to accurately reflect the statistical characteristics of the interfering radar parameters in dynamic scenarios. In this work, we propose a new method that treats interference mitigation as a source separation problem. We leverage score-based generative networks to explicitly learn the interfering radar parameters. These learned parameters are subsequently combined with Maximum-A-posteriori estimation, allowing for an algorithm with enhanced performance. We demonstrate that our algorithm outperforms the baselines in signal-To-noise ratio.