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 covariance matrix (R...
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| Дата: | 2006 |
|---|---|
| Автори: | , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Інститут програмних систем НАН України
2006
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| Онлайн доступ: | https://nasplib.isofts.kiev.ua/handle/123456789/1581 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | Data assimilation using kalman filter techniques / G. Dimitriu, R. Cuciureanu // Проблеми програмування. — 2006. — N 2-3. — С. 688-693. — Бібліогр.: 5 назв. — англ. |
Репозитарії
Digital Library of Periodicals of National Academy of Sciences of Ukraine| id |
nasplib_isofts_kiev_ua-123456789-1581 |
|---|---|
| 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 |
| institution |
Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| title |
Data assimilation using kalman filter techniques |
| spellingShingle |
Data assimilation using kalman filter techniques Dimitriu, G. Cuciureanu, R. Прикладне програмне забезпечення |
| title_short |
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_sort |
data assimilation using kalman filter techniques |
| author |
Dimitriu, G. Cuciureanu, R. |
| author_facet |
Dimitriu, G. Cuciureanu, R. |
| topic |
Прикладне програмне забезпечення |
| topic_facet |
Прикладне програмне забезпечення |
| publishDate |
2006 |
| language |
English |
| publisher |
Інститут програмних систем НАН України |
| format |
Article |
| 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.
|
| issn |
1727-4907 |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/1581 |
| 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. |
| work_keys_str_mv |
AT dimitriug dataassimilationusingkalmanfiltertechniques AT cuciureanur dataassimilationusingkalmanfiltertechniques |
| first_indexed |
2025-11-30T16:11:10Z |
| last_indexed |
2025-11-30T16:11:10Z |
| _version_ |
1850858080641744896 |