  A novel framework to enhance the angular resolution of automotive radars is
proposed. An approach to enlarge the antenna aperture using artificial neural
networks is developed using a self-supervised learning scheme. Data from a high
angular resolution radar, i.e., a radar with a large antenna aperture, is used
to train a deep neural network to extrapolate the antenna element's response.
Afterward, the trained network is used to enhance the angular resolution of
compact, low-cost radars. One million scenarios are simulated in a Monte-Carlo
fashion, varying the number of targets, their Radar Cross Section (RCS), and
location to evaluate the method's performance. Finally, the method is tested in
real automotive data collected outdoors with a commercial radar system. A
significant increase in the ability to resolve targets is demonstrated, which
can translate to more accurate and faster responses from the planning and
decision making system of the vehicle.
