Automotive Radar Signal Interference Mitigation Using RNN with Self Attention

Radar is a key component of autonomous driving. We can capture the target range and velocity using radar signal that is transmitted and reflected by a target. However, the noise floor increases when interference signals exist, which severely affects the detectability of target objects. For these reasons, many previous studies have proposed methods for cancelling interference or reconstructing original signals. However, little work has been done using deep learning algorithm on interference mitigation. In this paper, we propose a new method using deep learning. We improve the performance of the existing deep learning algorithm using attention mechanism. We applied our algorithm to the OFDM radar environment as well as the existing frequency modulated continuous wave(FMCW) radar. The experiment show our deep learning based method outperforms existing methods. The implementation of our paper is available at https://github.com/jwmun/Radar.