AirSAS: Controlled Dataset Generation for Physics-Informed Machine Learning

Benjamin Cowen, j daniel park, thomas e blanford, geoff goehle, daniel c brown

Abstract: Synthetic aperture sonar (SAS) is an underwater remote sensing technique for applications such as seafloor characterization and object detection. However, underwater SAS datasets are both extremely expensive to collect and difficult to control and repeat. We propose an in-air SAS measurement apparatus (AirSAS) made from commercial off-the-shelf laboratory equipment to generate controlled, repeatable datasets. AirSAS is both flexible and sufficiently delicate to capture the complex acoustic phenomena inherent in SAS measurements. The system allows us to physically control the differences between classes of interest, and observe acoustic phenomenology that is rare or expensive to collect underwater. Accordingly, we can measure and tune which acoustic phenomena deep learning models are sensitive to. AirSAS can generate both circular and linear track collections. The first iteration of the AirSAS dataset is currently under curation.