Source localization in an ocean waveguide using supervised machine learning
Abstract
Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix (SCM) and used as the input. Three machine learning methods (feed-forward neural networks (FNN), support vector machines (SVM) and random forests (RF)) are investigated in this paper, with focus on the FNN. The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization..
Cite
@article{arxiv.1701.08431,
title = {Source localization in an ocean waveguide using supervised machine learning},
author = {Haiqiang Niu and Emma Reeves and Peter Gerstoft},
journal= {arXiv preprint arXiv:1701.08431},
year = {2017}
}
Comments
Submitted to The Journal of the Acoustical Society of America