Dst 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 model parameters and updating recursively. Comparison between these techn...

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Main Authors:	Kaewkham-ai B., Harrison R.F.
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Online Access:	http://www.scopus.com/inward/record.url?eid=2-s2.0-77950868542&partnerID=40&md5=a1413faa0ca3be69b1453bb3fc602c63 http://cmuir.cmu.ac.th/handle/6653943832/1430
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Institution:	Chiang Mai University
Language:	English

id	th-cmuir.6653943832-1430
record_format	dspace
spelling	th-cmuir.6653943832-14302014-08-29T09:29:17Z Dst index prediction using joint and dual unscented Kalman filter Kaewkham-ai B. Harrison R.F. 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 model parameters and updating recursively. Comparison between these techniques 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. 2014-08-29T09:29:17Z 2014-08-29T09:29:17Z 2009 Conference Paper 9780889868083 79740 http://www.scopus.com/inward/record.url?eid=2-s2.0-77950868542&partnerID=40&md5=a1413faa0ca3be69b1453bb3fc602c63 http://cmuir.cmu.ac.th/handle/6653943832/1430 English
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
language	English
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 model parameters and updating recursively. Comparison between these techniques 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 or Workshop Item
author	Kaewkham-ai B. Harrison R.F.
spellingShingle	Kaewkham-ai B. Harrison R.F. Dst index prediction using joint and dual unscented Kalman filter
author_facet	Kaewkham-ai B. Harrison R.F.
author_sort	Kaewkham-ai B.
title	Dst index prediction using joint and dual unscented Kalman filter
title_short	Dst index prediction using joint and dual unscented Kalman filter
title_full	Dst index prediction using joint and dual unscented Kalman filter
title_fullStr	Dst index prediction using joint and dual unscented Kalman filter
title_full_unstemmed	Dst index prediction using joint and dual unscented Kalman filter
title_sort	dst index prediction using joint and dual unscented kalman filter
publishDate	2014
url	http://www.scopus.com/inward/record.url?eid=2-s2.0-77950868542&partnerID=40&md5=a1413faa0ca3be69b1453bb3fc602c63 http://cmuir.cmu.ac.th/handle/6653943832/1430
_version_	1681419669098987520

Dst index prediction using joint and dual unscented Kalman filter

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