Unsupervised learning of platform motion in synthetic aperture sonar

Abstract

Synthetic aperture sonar (SAS) provides high-resolution acoustic imaging by processing coherently the backscattered signal recorded over consecutive pings as the bearing platform moves along a predefined path. Coherent processing requires accurate estimation and compensation of the platform's motion for high quality imaging. The motion of the platform carrying the SAS system can be estimated by cross-correlating redundant recordings at successive pings due to the spatiotemporal coherence of statistically homogeneous backscatter. This data-driven approach for estimating the motion of the SAS platform is essential when positioning information from navigational instruments is absent or inadequately accurate. Herein, the problem of platform motion estimation from coherence measurements of diffuse backscatter is formulated in a probabilistic framework. A variational autoencoder is designed to disentangle the ping-to-ping platform displacement from three-dimensional (3D) spatiotemporal coherence measurements. Unsupervised representation learning from unlabeled data offers robust 3D platform motion estimation. Including a small amount of labeled data during training improves further the platform motion estimation accuracy.