Authors:
Mauren C. de Andrade
1
;
Matheus Nogueira
2
;
Eduardo Fidelis
2
;
Luiz Campos
2
;
Pietro Campos
2
;
Torsten Schön
3
and
Lester de Abreu Faria
2
Affiliations:
1
Universidade Tecnologica Federal do Parana, Ponta Grossa, Brazil
;
2
Centro Universitario Facens, Sorocaba, Brazil
;
3
AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany
Keyword(s):
Radar Application, Generative Adversarial Network, Ground-Based Radar Dataset, Synthetic Automotive Radar Data.
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.