Seabed classification and source localization with Gaussian processes and machine learning-Reference-Cited by-同舟云学术

Seabed classification and source localization with Gaussian processes and machine learning

Published:2022-08 Issue:8 Volume:2 Page:084801
ISSN:2691-1191
Container-title:JASA Express Letters
language:en
Short-container-title:JASA Express Letters

Author:

Frederick Christina¹,Michalopoulou Zoi-Heleni¹

Affiliation:

1. Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey 07102, USA,

Abstract

Workshop '97 data are employed for seabed classification and source range estimation. The data are acoustic fields computed at vertically separated receivers for various ranges and different environments. Gaussian processes are applied for denoising the data and predicting the field at virtual receivers, sampling the water column densely within the array aperture. The enhanced fields are used in combination with machine learning to map the signals to one of 15 sediment-range classes (corresponding to three environments and five ranges). The classification results after using Gaussian processes for denoising are superior to those when noisy workshop data are employed.

Funder

Office of Naval Research

Publisher

Acoustical Society of America (ASA)

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics

Link

https://asa.scitation.org/doi/pdf/10.1121/10.0013365

Reference15 articles.

1. Review of Geoacoustic Inversion in Underwater Acoustics

2. Source localization in an ocean waveguide using supervised machine learning

3. Seabed and range estimation of impulsive time series using a convolutional neural network

4. Seabed classification using physics-based modeling and machine learning

5. Seabed classification from merchant ship-radiated noise using a physics-based ensemble of deep learning algorithms

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