  The main approaches for simulating FMCW radar are based on ray tracing, which
is usually computationally intensive and do not account for background noise.
This work proposes a faster method for FMCW radar simulation capable of
generating synthetic raw radar data using generative adversarial networks
(GAN). The code and pre-trained weights are open-source and available on
GitHub. This method generates 16 simultaneous chirps, which allows the
generated data to be used for the further development of algorithms for
processing radar data (filtering and clustering). This can increase the
potential for data augmentation, e.g., by generating data in non-existent or
safety-critical scenarios that are not reproducible in real life. In this work,
the GAN was trained with radar measurements of a motorcycle and used to
generate synthetic raw radar data of a motorcycle traveling in a straight line.
For generating this data, the distance of the motorcycle and Gaussian noise are
used as input to the neural network. The synthetic generated radar chirps were
evaluated using the Frechet Inception Distance (FID). Then, the Range-Azimuth
(RA) map is calculated twice: first, based on synthetic data using this GAN
and, second, based on real data. Based on these RA maps, an algorithm with
adaptive threshold and edge detection is used for object detection. The results
have shown that the data is realistic in terms of coherent radar reflections of
the motorcycle and background noise based on the comparison of chirps, the RA
maps and the object detection results. Thus, the proposed method in this work
has shown to minimize the simulation-to-reality gap for the generation of radar
data.
