Title: 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..