|Title||Source localization in an ocean waveguide using supervised machine learning|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Niu H.Q, Reeves E., Gerstoft P|
|Journal||Journal of the Acoustical Society of America|
|Type of Article||Article|
|Keywords||acoustic 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.