Matched field source localization with Gaussian processes

Zoi Heleni Michalopoulou, Peter Gerstoft, Diego Caviedes-Nozal

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

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.

Original languageEnglish (US)
Article number064801
JournalJASA Express Letters
Volume1
Issue number6
DOIs
StatePublished - Jun 1 2021

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics
  • Music
  • Arts and Humanities (miscellaneous)

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