  Autonomous driving highly depends on capable sensors to perceive the
environment and to deliver reliable information to the vehicles' control
systems. To increase its robustness, a diversified set of sensors is used,
including radar sensors. Radar is a vital contribution of sensory information,
providing high resolution range as well as velocity measurements. The increased
use of radar sensors in road traffic introduces new challenges. As the so far
unregulated frequency band becomes increasingly crowded, radar sensors suffer
from mutual interference between multiple radar sensors. This interference must
be mitigated in order to ensure a high and consistent detection sensitivity. In
this paper, we propose the use of Complex-Valued Convolutional Neural Networks
(CVCNNs) to address the issue of mutual interference between radar sensors. We
extend previously developed methods to the complex domain in order to process
radar data according to its physical characteristics. This not only increases
data efficiency, but also improves the conservation of phase information during
filtering, which is crucial for further processing, such as angle estimation.
Our experiments show, that the use of CVCNNs increases data efficiency, speeds
up network training and substantially improves the conservation of phase
information during interference removal.
