Matched field source localization with Gaussian processes
Abstract
For a sparsely observed acoustic field, Gaussian processes can predict a densely sampled field on the array. The prediction quality depends on the choice of a kernel and a set of hyperparameters. Gaussian processes are applied to source localization in the ocean in combination with matched-field processing. Compared to conventional processing, the denser sampling of the predicted field across the array reduces the ambiguity function sidelobes. As the noise level increases, the Gaussian process–based processor has a distinctly higher probability of correct localization than conventional processing, due to both denoising and denser field prediction.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 01, 2021
- Source ID
- 10.1121/10.0005069
Entities
People
- Diego Caviedes-nozal
- Peter Gerstoft
- Zoi Heleni Michalopoulou
Organizations
- New Jersey Institute of Technology
- Office of Naval Research
- University of California, San Diego