# Source localization in an ocean waveguide using supervised machine learning

arXiv: https://arxiv.org/abs/1701.08431 · HTML: https://mlsanity.com/paper/1701.08431/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2017.

## Built on by (most cited first, 5 of 5)

- [A physically-informed Deep-Learning approach for locating sources in a waveguide](https://mlsanity.com/paper/2208.04938/) (2022)
- [Authentication by Location Tracking in Underwater Acoustic Networks](https://mlsanity.com/paper/2410.03511/) (2024)
- [Joint Source-Environment Adaptation of Data-Driven Underwater Acoustic Source Ranging Based on Model Uncertainty](https://mlsanity.com/paper/2503.23258/) (2025)
- [Mismatch-Robust Underwater Acoustic Localization Using A Differentiable Modular Forward Model](https://mlsanity.com/paper/2503.23260/) (2025)
- [Joint Source-Environment Adaptation for Deep Learning-Based Underwater Acoustic Source Ranging](https://mlsanity.com/paper/2503.23262/) (2025)

## Abstract (arXiv)

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..