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Lognormal and Mixed Gaussian–Lognormal Kalman Filters
by
Hossen, Md. Jakir
, Kliewer, Anton J.
, Forsythe, John M.
, Fletcher, Steven J.
, Goodliff, Michael R.
, Jones, Andrew S.
, Van Loon, Senne
, Zupanski, Milija
, Wu, Ting-Chi
in
Control theory
/ Data assimilation
/ Extended Kalman filter
/ Kalman filters
/ Normal distribution
/ Random variables
2023
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Lognormal and Mixed Gaussian–Lognormal Kalman Filters
by
Hossen, Md. Jakir
, Kliewer, Anton J.
, Forsythe, John M.
, Fletcher, Steven J.
, Goodliff, Michael R.
, Jones, Andrew S.
, Van Loon, Senne
, Zupanski, Milija
, Wu, Ting-Chi
in
Control theory
/ Data assimilation
/ Extended Kalman filter
/ Kalman filters
/ Normal distribution
/ Random variables
2023
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Do you wish to request the book?
Lognormal and Mixed Gaussian–Lognormal Kalman Filters
by
Hossen, Md. Jakir
, Kliewer, Anton J.
, Forsythe, John M.
, Fletcher, Steven J.
, Goodliff, Michael R.
, Jones, Andrew S.
, Van Loon, Senne
, Zupanski, Milija
, Wu, Ting-Chi
in
Control theory
/ Data assimilation
/ Extended Kalman filter
/ Kalman filters
/ Normal distribution
/ Random variables
2023
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Journal Article
Lognormal and Mixed Gaussian–Lognormal Kalman Filters
2023
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Overview
In this paper we present the derivation of two new forms of the Kalman filter equations; the first is for a pure lognormally distributed random variable, while the second set of Kalman filter equations will be for a combination of Gaussian and lognormally distributed random variables. We show that the appearance is similar to that of the Gaussian-based equations, but that the analysis state is a multivariate median and not the mean. We also show results of the mixed distribution Kalman filter with the Lorenz 1963 model with lognormal errors for the background and observations of the z component, and compare them to analysis results from a traditional Gaussian-based extended Kalman filter and show that under certain circumstances the new approach produces more accurate results.
Publisher
American Meteorological Society
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