  Automotive radars have an important role in autonomous driving systems. The
main challenge in automotive radar detection is the radar's wide point spread
function (PSF) in the angular domain that causes blurriness and clutter in the
radar image. Numerous studies suggest employing an 'end-to-end' learning
strategy using a Deep Neural Network (DNN) to directly detect objects from
radar images. This approach implicitly addresses the PSF's impact on objects of
interest. In this paper, we propose an alternative approach, which we term
"Boosting Radar Reflections" (BoostRad). In BoostRad, a first DNN is trained to
narrow the PSF for all the reflection points in the scene. The output of the
first DNN is a boosted reflection image with higher resolution and reduced
clutter, resulting in a sharper and cleaner image. Subsequently, a second DNN
is employed to detect objects within the boosted reflection image. We develop a
novel method for training the boosting DNN that incorporates domain knowledge
of radar's PSF characteristics. BoostRad's performance is evaluated using the
RADDet and CARRADA datasets, revealing its superiority over reference methods.
