  In automotive systems, a radar is a key component of autonomous driving.
Using transmit and reflected radar signal by a target, we can capture the
target range and velocity. However, when interference signals exist, noise
floor increases and it severely affects the detectability of target objects.
For these reasons, previous studies have been proposed to cancel interference
or reconstruct original signals. However, the conventional signal processing
methods for canceling the interference or reconstructing the transmit signals
are difficult tasks, and also have many restrictions. In this work, we propose
a novel approach to mitigate interference using deep learning. The proposed
method provides high performance in various interference conditions and has low
processing time. Moreover, we show that our proposed method achieves better
performance compared to existing signal processing methods.
