Seabed classification and source localization with Gaussian processes and machine learning
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 01, 2022
- Source ID
- 10.1121/10.0013365
Entities
People
- Christina Frederick
- Zoi Heleni Michalopoulou
Organizations
- New Jersey Institute of Technology
- Office of Naval Research