  In autonomous driving, radar systems play an important role in detecting
targets such as other vehicles on the road. Radars mounted on different cars
can interfere with each other, degrading the detection performance. Deep
learning methods for automotive radar interference mitigation can succesfully
estimate the amplitude of targets, but fail to recover the phase of the
respective targets. In this paper, we propose an efficient and effective
technique based on unfolded robust Principal Component Analysis (RPCA) that is
able to estimate both amplitude and phase in the presence of interference. Our
contribution consists in introducing residual overcomplete auto-encoder
(ROC-AE) blocks into the recurrent architecture of unfolded RPCA, which results
in a deeper model that significantly outperforms unfolded RPCA as well as other
deep learning models.
