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 model parameters and updating recursively. Comparison between these techn...
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2018
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th-cmuir.6653943832-490012018-08-16T02:12:51Z D<inf>st</inf> index prediction using joint and dual unscented Kalman filter Boonsri Kaewkham-ai Robert F. Harrison Computer Science Mathematics 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. 2018-08-16T02:08:15Z 2018-08-16T02:08:15Z 2009-12-01 Conference Proceeding 2-s2.0-77950868542 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77950868542&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49001 |
| institution |
Chiang Mai University |
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Chiang Mai University Library |
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Thailand |
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CMU Intellectual Repository |
| topic |
Computer Science Mathematics |
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Computer Science Mathematics 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 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 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=77950868542&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49001 |
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1681423331540074496 |