  While camera and LiDAR processing have been revolutionized since the
introduction of deep learning, radar processing still relies on classical
tools. In this paper, we introduce a deep learning approach for radar
processing, working directly with the radar complex data. To overcome the lack
of radar labeled data, we rely in training only on the radar calibration data
and introduce new radar augmentation techniques. We evaluate our method on the
radar 4D detection task and demonstrate superior performance compared to the
classical approaches while keeping real-time performance. Applying deep
learning on radar data has several advantages such as eliminating the need for
an expensive radar calibration process each time and enabling classification of
the detected objects with almost zero-overhead.
