  Deployment and operation of autonomous underwater vehicles is expensive and
time-consuming. High-quality realistic sonar data simulation could be of
benefit to multiple applications, including training of human operators for
post-mission analysis, as well as tuning and validation of autonomous target
recognition (ATR) systems for underwater vehicles. Producing realistic
synthetic sonar imagery is a challenging problem as the model has to account
for specific artefacts of real acoustic sensors, vehicle altitude, and a
variety of environmental factors. We propose a novel method for generating
realistic-looking sonar side-scans of full-length missions, called Markov
Conditional pix2pix (MC-pix2pix). Quantitative assessment results confirm that
the quality of the produced data is almost indistinguishable from real.
Furthermore, we show that bootstrapping ATR systems with MC-pix2pix data can
improve the performance. Synthetic data is generated 18 times faster than real
acquisition speed, with full user control over the topography of the generated
data.
