Particle Kalman Filtering For Ocean State Estimation

Abstract

The long-term scientific objective is to develop a fully nonlinear Bayesian approach that generalizes the optimality of ensemble Kalman filter methods to nonlinear systems and can be suitable for large dimensional data assimilation problems. The approach will be tested with realistic applications to ocean data assimilation problems. The goal of this research is to explore a new direction that can lead to fully nonlinear filters that can perform better than the ensemble Kalman filter (EnKF) methods with highly nonlinear systems at reasonable computational requirements. We aim at proposing, implementing and testing new nonlinear Kalman filters with ocean data assimilation problems in mind. Simple nonlinear dynamical models will be first considered to evaluate the behavior of these new filters and assess their efficiency compared to the EnKF methods.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
ADA527120

Entities

People

  • Bruce D. Cornuelle
  • Ibrahim Hoteit

Organizations

  • Scripps Institution of Oceanography

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Assimilation
  • Data Science
  • Distribution Functions
  • Filters
  • Filtration
  • Gaussian Distributions
  • Information Science
  • Kalman Filtering
  • Kalman Filters
  • Nonlinear Systems
  • Particles
  • Probability
  • Probability Distributions
  • Sequential Monte Carlo Methods
  • Statistical Analysis
  • Statistics

Readers

  • Approximation Theory.
  • Distributed Systems and Data Platform Development
  • Marine Ecotoxicology

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms