arXiv Open Access 2017

Source localization in an ocean waveguide using supervised machine learning

Haiqiang Niu Emma Reeves Peter Gerstoft

Lihat Sumber

Abstrak

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..

Topik & Kata Kunci

physics.ao-ph cs.NE physics.geo-ph

Penulis (3)

Haiqiang Niu

Emma Reeves

Peter Gerstoft

Format Sitasi

APA MLA BibTeX

Niu, H., Reeves, E., Gerstoft, P. (2017). Source localization in an ocean waveguide using supervised machine learning. https://arxiv.org/abs/1701.08431

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓