Matched field source localization with Gaussian processes

Authors

Zoi Heleni Michalopoulou, New Jersey Institute of Technology
Peter Gerstoft, University of California, San Diego
Diego Caviedes-Nozal, Technical University of Denmark

Document Type

Article

Publication Date

6-1-2021

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.

Identifier

85118570653 (Scopus)

Publication Title

Jasa Express Letters

External Full Text Location

https://doi.org/10.1121/10.0005069

ISSN

26911191

Issue

6

Volume

1

Grant

N00014-18-1-2118

Fund Ref

Office of Naval Research

Recommended Citation

Michalopoulou, Zoi Heleni; Gerstoft, Peter; and Caviedes-Nozal, Diego, "Matched field source localization with Gaussian processes" (2021). Faculty Publications. 4071.
https://digitalcommons.njit.edu/fac_pubs/4071