Semi-supervised Learning Method for Source Range Estimation in Shallow Water

Data-driven machine learning methods in recent years have shown great vitality in underwater acoustic, especially in the localization of underwater sound sources. Meanwhile, there exists a great difficulty in the acquisition and processing of underwater acoustic data, which caused the lack of label data. This paper proposes a two-step semi-supervised learning-based range estimation method for shallow ocean sound sources, which utilizes semi-supervised learning in deep learning to alleviate the problem of limited access to underwater acoustic label data and relatively abundant unlabelled data in practical scenarios. The performance of the method has been validated on SWellEx-96 experiment data.