  High resolution automotive radar sensors are required in order to meet the
high bar of autonomous vehicles needs and regulations. However, current radar
systems are limited in their angular resolution causing a technological gap. An
industry and academic trend to improve angular resolution by increasing the
number of physical channels, also increases system complexity, requires
sensitive calibration processes, lowers robustness to hardware malfunctions and
drives higher costs. We offer an alternative approach, named Radar signal
Reconstruction using Self Supervision (R2-S2), which significantly improves the
angular resolution of a given radar array without increasing the number of
physical channels. R2-S2 is a family of algorithms which use a Deep Neural
Network (DNN) with complex range-Doppler radar data as input and trained in a
self-supervised method using a loss function which operates in multiple data
representation spaces. Improvement of 4x in angular resolution was demonstrated
using a real-world dataset collected in urban and highway environments during
clear and rainy weather conditions.
