Machine Learning

Source localization in an ocean waveguide using supervised machine learning

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 1 year, 6 months ago.


  • arXiv
    5 pts

    Source localization in an ocean waveguide using supervised machine learning

    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..

    Source localization in an ocean waveguide using supervised machine learning
    by Haiqiang Niu, Emma Reeves, Peter Gerstoft
    https://arxiv.org/pdf/1701.08431v4.pdf

You must be logged in to reply to this topic.