Particle Kalman Filtering for Ocean State Estimation

Abstract

New nonlinear filtering algorithms were developed and are currently being tested. Numerical results suggest that nonlinear filters behave better than the ensemble Kalman filter methods with strongly nonlinear systems. They also seem to respect the dynamical of the system state more resulting in more stable predictions.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2010
Accession Number
ADA546730

Entities

People

  • Aneesh Subramanian
  • Bruce D. Cornuelle
  • Ibrahim Hoteit

Organizations

  • Scripps Institution of Oceanography

Tags

DTIC Thesaurus Topics

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

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.