Source localization in an ocean waveguide using supervised machine learning
نویسندگان
چکیده
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.
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Source localization in an ocean waveguide using supervised machine learning
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عنوان ژورنال:
- The Journal of the Acoustical Society of America
دوره 142 3 شماره
صفحات -
تاریخ انتشار 2017