Machine learning and sparse processing in support of geoacoustic inversion

Abstract

Our objectives include the development of new geoacoustic inversion methods, their use in the analysis of shallow water experimental data, and evaluation of geoacoustic model parameter uncertainties including the mapping of these uncertainties through to system performance uncertainties. Of specific technical interest are the development of methods to estimate and track environmental parameters using: (1) sparse sampling, (2) machine learning, (3) noise inversion, (4) graph-based estimation, and (5) geoacoustic inversion.This abstract is approved for public release

Document Details

Document Type
DoD Grant Award
Publication Date
Apr 06, 2021
Source ID
N000142112267

Entities

People

  • Peter Gerstoft

Organizations

  • Office of Naval Research
  • United States Navy
  • University of California, San Diego

Tags

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Clinical Trial Research.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference