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Hybrid Methods in Engineering

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ISSN Печать: 1099-2391

ISSN Онлайн: 2641-7359

DATA ASSIMILATION USING AN ADAPTIVE KALMAN FILTER AND LAPLACE TRANSFORM

Том 2, Выпуск 3, 2000, 20 pages
DOI: 10.1615/HybMethEng.v2.i3.50
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Краткое описание

An Adaptive Extended Kalman Filter is used for data assimilation in two nonlinear dynamical systems: the Lorenz system in chaotic state and the computational model DYNAMO for the atmosphere. This approach does not require the modeling error to be stationary and uses a Linear Kalman Filter to estimate this error. This method is compared to the methods using Laplace transform, and Linear and Extended Kalman Filters. The conclusion was that the choice between using Laplace transform and Adaptive Kalman Filter assimilation methods for DYNAMO depended on whether one was willing to completely reject high-frequency information or not. When that information was considered useless, the Laplace filtering eliminated it better than the Kalman filtering. Otherwise, Kalman assimilated it better than Laplace.

ЦИТИРОВАНО В
  1. Furtado Helaine Cristina Morais, Velho Haroldo Fraga de Campos, Macau Elbert Einstein Nehrer, Data assimilation: Particle filter and artificial neural networks, Journal of Physics: Conference Series, 135, 2008. Crossref

  2. Härter Fabrício P., de Campos Velho Haroldo F., Rempel Erico L., Chian Abraham C.-L., Neural networks in auroral data assimilation, Journal of Atmospheric and Solar-Terrestrial Physics, 70, 10, 2008. Crossref

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