Interference Mitigation for Automotive FMCW Radar Based on Contrastive Learning With Dilated Convolution

Abstract

As one of the crucial sensors for environment sensing, frequency modulated continuous wave (FMCW) radars are widely used in modern vehicles for driving assistance/autonomous driving. However, the limited frequency bandwidth and the increasing number of equipped radar sensors would inevitably cause mutual interference, degrading target detection and producing safety hazards. In this paper, a deep learning-based interference mitigation (IM) approach is proposed for FMCW radars by using the dilated convolution for network construction and a designated contrast learning strategy for training. The dilated convolution enlarges the receptive field of the neural network, and the designated contrastive learning strategy enforces to distinguish better between interferences and desired signals. The results of numerical simulation and experimental data processing show that the dilated convolution-based IM network, compared to the traditional convolution-based ones, can achieve a higher Signal-to-Interference-plus-Noise-Ratio (SINR) and target detection rate. Moreover, the designated contrastive learning strategy enables a better and more stable IM performance without increasing the complexity of the network, which can facilitate faster signal processing.