  As autonomous vehicles and advanced driving assistance systems have entered
wider deployment, there is an increased interest in building robust perception
systems using radars. Radar-based systems are lower cost and more robust to
adverse weather conditions than their LiDAR-based counterparts; however the
point clouds produced are typically noisy and sparse by comparison. In order to
combat these challenges, recent research has focused on consuming the raw radar
data, instead of the final radar point cloud. We build on this line of work and
demonstrate that by bringing elements of the signal processing pipeline into
our network and then pre-training on the signal processing task, we are able to
achieve state of the art detection performance on the RADIal dataset. Our
method uses expensive offline signal processing algorithms to pseudo-label data
and trains a network to distill this information into a fast convolutional
backbone, which can then be finetuned for perception tasks. Extensive
experiment results corroborate the effectiveness of the proposed techniques.
