  In this paper we present a novel simulation technique for generating high
quality images of any predefined resolution. This method can be used to
synthesize sonar scans of size equivalent to those collected during a
full-length mission, with across track resolutions of any chosen magnitude. In
essence, our model extends Generative Adversarial Networks (GANs) based
architecture into a conditional recursive setting, that facilitates the
continuity of the generated images. The data produced is continuous,
realistically-looking, and can also be generated at least two times faster than
the real speed of acquisition for the sonars with higher resolutions, such as
EdgeTech. The seabed topography can be fully controlled by the user. The visual
assessment tests demonstrate that humans cannot distinguish the simulated
images from real. Moreover, experimental results suggest that in the absence of
real data the autonomous recognition systems can benefit greatly from training
with the synthetic data, produced by the R2D2-GANs.
