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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2018
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th-cmuir.6653943832-594942018-09-10T03:16:18Z 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. 2018-09-10T03:16:18Z 2018-09-10T03:16:18Z 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/6653943832/59494 |
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Computer Science Boonsri Kaewkham-Ai Robert F. Harrison D<inf>ST</inf> index prediction using joint and dual unscented Kalman filter |
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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/6653943832/59494 |
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