  Simulating realistic radar data has the potential to significantly accelerate
the development of data-driven approaches to radar processing. However, it is
fraught with difficulty due to the notoriously complex image formation process.
Here we propose to learn a radar sensor model capable of synthesising faithful
radar observations based on simulated elevation maps. In particular, we adopt
an adversarial approach to learning a forward sensor model from unaligned radar
examples. In addition, modelling the backward model encourages the output to
remain aligned to the world state through a cyclical consistency criterion. The
backward model is further constrained to predict elevation maps from real radar
data that are grounded by partial measurements obtained from corresponding
lidar scans. Both models are trained in a joint optimisation. We demonstrate
the efficacy of our approach by evaluating a down-stream segmentation model
trained purely on simulated data in a real-world deployment. This achieves
performance within four percentage points of the same model trained entirely on
real data.
