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In autonomous driving, radar systems play an important role in detectingtargets such as other vehicles on the road. Radars mounted on different carscan interfere with each other, degrading the detection performance. Deeplearning methods for automotive radar interference mitigation can succesfullyestimate the amplitude of targets, but fail to recover the phase of therespective targets. In this paper, we propose an efficient and effectivetechnique based on unfolded robust Principal Component Analysis (RPCA) that isable to estimate both amplitude and phase in the presence of interference. Ourcontribution consists in introducing residual overcomplete auto-encoder(ROC-AE) blocks into the recurrent architecture of unfolded RPCA, which resultsin a deeper model that significantly outperforms unfolded RPCA as well as otherdeep learning models.

Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks