A heuristic Kalman filter for a class of nonlinear systems
One of the basic assumptions involved in the "optimality" of the Kalman filter theory is that the system under consideration must be linear. If the model is nonlinear, a linearization procedure is usually performed in deriving the filtering equations. This approach requires the nonlinear s...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | article |
| منشور في: |
2004
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/11179 http://dx.doi.org/10.1109/TAC.2004.838485 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/1369403 |
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| _version_ | 1864513467524841472 |
|---|---|
| author | Saab, Samer S. |
| author_facet | Saab, Samer S. |
| author_role | author |
| dc.creator.none.fl_str_mv | Saab, Samer S. |
| dc.date.none.fl_str_mv | 2004 2019-07-31T10:09:07Z 2019-07-31T10:09:07Z 2019-07-31 |
| dc.identifier.none.fl_str_mv | 0018-9286 http://hdl.handle.net/10725/11179 http://dx.doi.org/10.1109/TAC.2004.838485 Saab, S. S. (2004). A heuristic Kalman filter for a class of nonlinear systems. IEEE transactions on automatic control, 49(12), 2261-2265. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/1369403 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | IEEE Transactions on Automatic Control |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | A heuristic Kalman filter for a class of nonlinear systems |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | One of the basic assumptions involved in the "optimality" of the Kalman filter theory is that the system under consideration must be linear. If the model is nonlinear, a linearization procedure is usually performed in deriving the filtering equations. This approach requires the nonlinear system dynamics to be differentiable. This note is an attempt to develop a heuristic Kalman filter for a class of nonlinear systems, with bounded first-order growth, that does not require the system dynamics to be differentiable. The proposed filter approximates the nonlinear state function by its state argument multiplied by a particular gain matrix only in the recursion of the estimation error covariance matrix. Under certain conditions, the error covariance remains bounded by bounds which can be precomputed from noise and system models, and the upper bound tends to zero when the state noise covariance tends to zero. A numerical example, with backlash nonlinearity, is also added. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_6f083bc221e9da8be5d2acf6d6d3a902 |
| identifier_str_mv | 0018-9286 Saab, S. S. (2004). A heuristic Kalman filter for a class of nonlinear systems. IEEE transactions on automatic control, 49(12), 2261-2265. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/11179 |
| publishDate | 2004 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A heuristic Kalman filter for a class of nonlinear systemsSaab, Samer S.One of the basic assumptions involved in the "optimality" of the Kalman filter theory is that the system under consideration must be linear. If the model is nonlinear, a linearization procedure is usually performed in deriving the filtering equations. This approach requires the nonlinear system dynamics to be differentiable. This note is an attempt to develop a heuristic Kalman filter for a class of nonlinear systems, with bounded first-order growth, that does not require the system dynamics to be differentiable. The proposed filter approximates the nonlinear state function by its state argument multiplied by a particular gain matrix only in the recursion of the estimation error covariance matrix. Under certain conditions, the error covariance remains bounded by bounds which can be precomputed from noise and system models, and the upper bound tends to zero when the state noise covariance tends to zero. A numerical example, with backlash nonlinearity, is also added.PublishedN/A2019-07-31T10:09:07Z2019-07-31T10:09:07Z20042019-07-31Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article0018-9286http://hdl.handle.net/10725/11179http://dx.doi.org/10.1109/TAC.2004.838485Saab, S. S. (2004). A heuristic Kalman filter for a class of nonlinear systems. IEEE transactions on automatic control, 49(12), 2261-2265.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://ieeexplore.ieee.org/abstract/document/1369403enIEEE Transactions on Automatic Controlinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/111792021-03-19T10:47:36Z |
| spellingShingle | A heuristic Kalman filter for a class of nonlinear systems Saab, Samer S. |
| status_str | publishedVersion |
| title | A heuristic Kalman filter for a class of nonlinear systems |
| title_full | A heuristic Kalman filter for a class of nonlinear systems |
| title_fullStr | A heuristic Kalman filter for a class of nonlinear systems |
| title_full_unstemmed | A heuristic Kalman filter for a class of nonlinear systems |
| title_short | A heuristic Kalman filter for a class of nonlinear systems |
| title_sort | A heuristic Kalman filter for a class of nonlinear systems |
| url | http://hdl.handle.net/10725/11179 http://dx.doi.org/10.1109/TAC.2004.838485 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/1369403 |