Modeling and Simulation of Sidescan Using Conditional Generative Adversarial Network

Abstract

Sidescan sonar has been used for maritime surveys since the mid-20th century. Due to its wide swath coverage and the sharpness of the produced images, it is an invaluable tool still to this day. When simulating sidescan data, there is a tradeoff between the quality of the produced images, the fidelity of the environment simulation, and the complexity of the sidescan model. In this article, we propose data-driven models as a way of removing some of this tradeoff. Using recently proposed conditional generative adversarial nets, we create a generative model that takes the environment as input, and produces realistic sidescan measurements. We show that end-to-end learning of flexible models allows simulating more complex sidescan data than would otherwise be possible given only geometric bathymetry.