  Simulation is an invaluable tool for radio-frequency system designers that
enables rapid prototyping of various algorithms for imaging, target detection,
classification, and tracking. However, simulating realistic radar scans is a
challenging task that requires an accurate model of the scene, radio frequency
material properties, and a corresponding radar synthesis function. Rather than
specifying these models explicitly, we propose DART - Doppler Aided Radar
Tomography, a Neural Radiance Field-inspired method which uses radar-specific
physics to create a reflectance and transmittance-based rendering pipeline for
range-Doppler images. We then evaluate DART by constructing a custom data
collection platform and collecting a novel radar dataset together with accurate
position and instantaneous velocity measurements from lidar-based localization.
In comparison to state-of-the-art baselines, DART synthesizes superior radar
range-Doppler images from novel views across all datasets and additionally can
be used to generate high quality tomographic images.
