Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/59494
Title: D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter
Authors: Boonsri Kaewkham-Ai
Robert F. Harrison
Keywords: Computer Science
Issue Date: 1-Dec-2009
Abstract: This paper presents a short-term prediction of the disturbance storm time (DST) index using unscented Kalman filter. Joint and dual estimation methods are studied to examine an improvement of DST index prediction by estimating modal parameters and updating recursively. Comparison between these teachniquies and a fixed model parameter prediction are made in terms of root mean square error (rmse). It is found that joint and dual estimation methods give less rmse than state estimation alone for all DST range, whereas state estimation alone shows better performance than joint and dual estimation for DST below-80 nT.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77954180429&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/59494
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.


Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.