D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter

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 teach...

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Main Authors:	Boonsri Kaewkham-Ai, Robert F. Harrison
Format:	Conference Proceeding
Published:	2018
Subjects:	Computer Science
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77954180429&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49000
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-49000
record_format	dspace
spelling	th-cmuir.6653943832-490002018-08-16T02:08:14Z D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter Boonsri Kaewkham-Ai Robert F. Harrison Computer Science 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. 2018-08-16T02:08:14Z 2018-08-16T02:08:14Z 2009-12-01 Conference Proceeding 2-s2.0-77954180429 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77954180429&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49000
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Computer Science
spellingShingle	Computer Science Boonsri Kaewkham-Ai Robert F. Harrison D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter
description	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.
format	Conference Proceeding
author	Boonsri Kaewkham-Ai Robert F. Harrison
author_facet	Boonsri Kaewkham-Ai Robert F. Harrison
author_sort	Boonsri Kaewkham-Ai
title	D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter
title_short	D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter
title_full	D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter
title_fullStr	D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter
title_full_unstemmed	D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter
title_sort	d<inf>st</inf> index prediction using joint and dual unscented kalman filter
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77954180429&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49000
_version_	1681423331360768000

D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter

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