Angle-Equivariant Convolutional Neural Networks for Interference Mitigation in Automotive Radar
This addresses interference mitigation for automotive radar systems, offering improved generalization across angles of arrival, but it is incremental as it builds on existing neural network approaches.
The paper tackles the problem of mutual interference in automotive radar sensors, which impairs prediction quality, by introducing a fully convolutional neural network with rank-three convolutions that achieves angle equivariance, outperforming previous work with higher robustness and fewer parameters.
In automotive applications, frequency modulated continuous wave (FMCW) radar is an established technology to determine the distance, velocity and angle of objects in the vicinity of the vehicle. The quality of predictions might be seriously impaired if mutual interference between radar sensors occurs. Previous work processes data from the entire receiver array in parallel to increase interference mitigation quality using neural networks (NNs). However, these architectures do not generalize well across different angles of arrival (AoAs) of interferences and objects. In this paper we introduce fully convolutional neural network (CNN) with rank-three convolutions which is able to transfer learned patterns between different AoAs. Our proposed architecture outperforms previous work while having higher robustness and a lower number of trainable parameters. We evaluate our network on a diverse data set and demonstrate its angle equivariance.