Source localization in an ocean waveguide using supervised machine learning

TitleSource localization in an ocean waveguide using supervised machine learning
Publication TypeJournal Article
Year of Publication2017
AuthorsNiu H.Q, Reeves E., Gerstoft P
JournalJournal of the Acoustical Society of America
Date Published2017/09
Type of ArticleArticle
ISBN Number0001-4966
Accession NumberWOS:000412100700016
Keywordsacoustic source localization; array; environment; fields; inversion; likelihood; neural-networks; parameters; recognition; shallow-water

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 and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). 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. (C) 2017 Acoustical Society of America.

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