Mitigating Interference for Automotive Millimeter-Wave Radar Perception in Dense Traffic Scenarios

Automotive Millimeter-wave (mmWave) radar is becoming an essential modality for autonomous vehicles to enable all-weather perception, especially when LiDAR and camera fail in foggy, rainy, or snowy conditions. It is expected that the mutual interference among multiple radars becomes a critical issue in dense traffic scenarios, which can severely degrade the radar performance and lead to accidents. Despite extensive interference mitigation techniques, none can meet the less valid signal distortion while high robustness requirements for automotive radar perception in dense traffic scenarios. To overcome this predicament, we propose mmMic, a novel multiple mutual interference mitigation system that can accurately separate interference and recover valid signals to maintain the reliability of the radar measurements. The key insight is to design an interference estimator that can accurately localize the interference signal according to its linear frequency modulation features in the time-frequency (TF) domain. In addition, mmMic also fully exploits undisturbed valid signal information within an extended time-frequency domain to reconstruct the damaged signal. Our experiments on a real testbed show that mmMic can improve SINR to interference-free levels from multiple radars, achieving an average SINR improvement of 17% compared to the best-performing baseline.