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...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | وقائع المؤتمر |
| منشور في: |
2018
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77950868542&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49001 |
| الوسوم: |
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| الملخص: | 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. |
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