# Source localization in an ocean waveguide using supervised machine learning

## TL;DR

This paper explores the use of supervised machine learning methods, especially neural networks, support vector machines, and random forests, for localizing acoustic sources in ocean environments using data-driven approaches.

## Contribution

It introduces a machine learning framework for source localization in ocean acoustics, comparing neural networks, SVM, and RF with traditional methods, focusing on the FNN's performance.

## Key findings

- FNN outperforms SVM and RF in range estimation accuracy.
- Machine learning methods show potential to improve underwater source localization.
- Results demonstrate comparable or better performance than conventional matched-field processing.

## 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..