Data assimilation using kalman filter techniques
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
 cova...
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| Date: | 2006 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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Інститут програмних систем НАН України
2006
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| Online Access: | https://nasplib.isofts.kiev.ua/handle/123456789/1581 |
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| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | Data assimilation using kalman filter techniques / G. Dimitriu, R. Cuciureanu // Проблеми програмування. — 2006. — N 2-3. — С. 688-693. — Бібліогр.: 5 назв. — англ. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| _version_ | 1862634126242217984 |
|---|---|
| author | Dimitriu, G. Cuciureanu, R. |
| author_facet | Dimitriu, G. Cuciureanu, R. |
| citation_txt | Data assimilation using kalman filter techniques / G. Dimitriu, R. Cuciureanu // Проблеми програмування. — 2006. — N 2-3. — С. 688-693. — Бібліогр.: 5 назв. — англ. |
| collection | DSpace DC |
| 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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| first_indexed | 2025-11-30T16:11:10Z |
| format | Article |
| fulltext | |
| id | nasplib_isofts_kiev_ua-123456789-1581 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1727-4907 |
| language | English |
| last_indexed | 2025-11-30T16:11:10Z |
| publishDate | 2006 |
| publisher | Інститут програмних систем НАН України |
| record_format | dspace |
| spelling | Dimitriu, G. Cuciureanu, R. 2008-08-26T13:22:57Z 2008-08-26T13:22:57Z 2006 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 https://nasplib.isofts.kiev.ua/handle/123456789/1581 004.75 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. en Інститут програмних систем НАН України Прикладне програмне забезпечення Data assimilation using kalman filter techniques Article published earlier |
| spellingShingle | Data assimilation using kalman filter techniques Dimitriu, G. Cuciureanu, R. Прикладне програмне забезпечення |
| title | Data assimilation using kalman filter techniques |
| title_full | Data assimilation using kalman filter techniques |
| title_fullStr | Data assimilation using kalman filter techniques |
| title_full_unstemmed | Data assimilation using kalman filter techniques |
| title_short | Data assimilation using kalman filter techniques |
| title_sort | data assimilation using kalman filter techniques |
| topic | Прикладне програмне забезпечення |
| topic_facet | Прикладне програмне забезпечення |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/1581 |
| work_keys_str_mv | AT dimitriug dataassimilationusingkalmanfiltertechniques AT cuciureanur dataassimilationusingkalmanfiltertechniques |