Model validation for simulated synthetic aperture sonar time series data

Abstract

Synthetic aperture sonar (SAS) systems have grown in popularity due to their ability to create high-resolution sonar imagery with a high area coverage rate. Researchers continue to find new uses for SAS data products. To support the development of new uses and algorithms, it is important to be able to accurately simulate SAS systems with synthetic time series data. This simulated data can be used for many purposes, including estimating system signal-to-noise ratio and performance in a variety of environments, testing signal processing algorithms, and training algorithms to recognize objects in SAS imagery. For these processing tasks to be successful, the simulator must accurately represent physical phenomena in the oceanic underwater environment, which can be extremely complex. This presentation will outline a process for validation and verification of a high-frequency SAS time series simulator, and discuss several useful tests for validating model fidelity. Two fundamental concepts will be explored in depth: the sonar equation and spatial coherence theory. The sonar equation is important because it accounts for correct implementation of multiple lower level models such as geometry, beam patterns, propagation, and seafloor scattering strength. Spatial coherence is important because it is relied on to form coherent imagery.