Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks
This addresses interference mitigation in radar systems for autonomous vehicles, offering an incremental improvement over prior deep learning approaches.
The paper tackled the problem of automotive radar interference, which degrades detection in autonomous driving, by proposing an unfolded robust PCA method with residual overcomplete auto-encoder blocks, resulting in a model that significantly outperforms existing deep learning methods in estimating both amplitude and phase.
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.