Angle-Equivariant Convolutional Neural Networks for Interference
  Mitigation in Automotive Radar
Christian Oswald Mate Toth Paul Meissner Franz Pernkopf
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.