Exploiting GAN Capacity to Generate Synthetic Automotive Radar Data

Mauren Louise Sguario C. Andrade, Matheus Velloso Nogueira, Eduardo Candioto Fidelis, Luiz Henrique Aguiar Campos, Pietro Lo Presti Campos, Torsten Schön, Lester de Abreu Faria

Abstract: In this paper, we evaluate the training of GAN for synthetic RAD image generation for four objects reflected by Frequency Modulated Continuous Wave radar: car, motorcycle, pedestrian and truck. This evaluation adds a new possibility for data augmentation when radar data labeling available is not enough. The results show that, yes, the GAN generated RAD images well, even when a specific class of the object is necessary. We also compared the scores of three GAN architectures, GAN Vanilla, CGAN, and DCGAN, in RAD synthetic imaging generation. We show that the generator can produce RAD images well enough with the results analyzed.