Source localization in an ocean waveguide using supervised machine learning
This work addresses source localization for underwater acoustics applications, but it is incremental as it applies existing machine learning methods to a known problem.
The paper tackled source localization in ocean acoustics by applying machine learning methods to estimate source ranges from acoustic data, showing that feed-forward neural networks, support vector machines, and random forests can achieve results comparable to conventional matched-field processing in the Noise09 experiment.
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..