  Radar sensors are gradually becoming a wide-spread equipment for road
vehicles, playing a crucial role in autonomous driving and road safety. The
broad adoption of radar sensors increases the chance of interference among
sensors from different vehicles, generating corrupted range profiles and
range-Doppler maps. In order to extract distance and velocity of multiple
targets from range-Doppler maps, the interference affecting each range profile
needs to be mitigated. In this paper, we propose a fully convolutional neural
network for automotive radar interference mitigation. In order to train our
network in a real-world scenario, we introduce a new data set of realistic
automotive radar signals with multiple targets and multiple interferers. To our
knowledge, we are the first to apply weight pruning in the automotive radar
domain, obtaining superior results compared to the widely-used dropout. While
most previous works successfully estimated the magnitude of automotive radar
signals, we propose a deep learning model that can accurately estimate the
phase. For instance, our novel approach reduces the phase estimation error with
respect to the commonly-adopted zeroing technique by half, from 12.55 degrees
to 6.58 degrees. Considering the lack of databases for automotive radar
interference mitigation, we release as open source our large-scale data set
that closely replicates the real-world automotive scenario for multiple
interference cases, allowing others to objectively compare their future work in
this domain. Our data set is available for download at:
http://github.com/ristea/arim-v2.
