Machine and deep learning approaches to localization and range estimation of underwater acoustic sources

Abstract

This paper introduces ongoing experiments and early results for the underwater localization and range estimation of acoustic sources. Beyond classical results obtained for direction of arrival estimation, results concerning range estimation using supervised learning with neural networks having both shallow and deep architectures are presented. The developed method is applicable in the context of a single sensor, a compact array, or a small aperture towed array and provided results with great potential both for industrial impact and for the conservation and density estimation of cetaceans. With an average error of 4.3% and 3.5%-respectively for a shallow and for a deep pre-trained architecture-for ranges up to 8 kilometers and consistently below 300 meters, the system provides robust estimates suitable for an automated real-time solution.