Data assimilation using kalman filter techniques
Vernadsky National Library of Ukraine
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Data assimilation using kalman filter techniques
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| Creator |
Dimitriu, G.
Cuciureanu, R. |
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| Subject |
Прикладне програмне забезпечення
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| Description |
Kalman filtering represents a powerful framework for solving data assimilation problems. Of interest here are the low-rank filters which are computationally efficient to solve large scale data assimilation problems. The low-rank filters are either based on factorization of the covariance matrix (RRSQRT filter), or approximation of statistics from a finite ensemble (ENKF). A new direction in filter implementation is the use of two filters next to each other of the same form or hybrid (POENKF). The factorization approach is based on the linear Kalman filter which can be extended towards nonlinear models. In this paper, the background, implementation and performance of some common used low-rank filters is discussed. Numerical results are presented. |
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| Date |
2008-08-26T13:22:57Z
2008-08-26T13:22:57Z 2006 |
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| Type |
Article
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| Identifier |
Data assimilation using kalman filter techniques / G. Dimitriu, R. Cuciureanu // Проблеми програмування. — 2006. — N 2-3. — С. 688-693. — Бібліогр.: 5 назв. — англ.
1727-4907 http://dspace.nbuv.gov.ua/handle/123456789/1581 004.75 |
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| Language |
en
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| Publisher |
Інститут програмних систем НАН України
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