  We propose a novel method to generate underwater object imagery that is
acoustically compliant with that generated by side-scan sonar using the Unreal
Engine. We describe the process to develop, tune, and generate imagery to
provide representative images for use in training automated target recognition
(ATR) and machine learning algorithms. The methods provide visual
approximations for acoustic effects such as back-scatter noise and acoustic
shadow, while allowing fast rendering with C++ actor in UE for maximizing the
size of potential ATR training datasets. Additionally, we provide analysis of
its utility as a replacement for actual sonar imagery or physics-based sonar
data.
