A recursive linear MMSE filter for dynamic systems with unknown state vector means
In this contribution we extend Kalman-filter theory by introducing a new recursive linear minimum mean squared error (MMSE) filter for dynamic systems with unknown state-vector means. The recursive filter enables the joint MMSE prediction and estimation of the random state vectors and their unknown...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
Springer
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/35782 |
| _version_ | 1848754589898113024 |
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| author | Khodabandeh, A. Teunissen, Peter |
| author_facet | Khodabandeh, A. Teunissen, Peter |
| author_sort | Khodabandeh, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this contribution we extend Kalman-filter theory by introducing a new recursive linear minimum mean squared error (MMSE) filter for dynamic systems with unknown state-vector means. The recursive filter enables the joint MMSE prediction and estimation of the random state vectors and their unknown means, respectively. We show how the new filter reduces to the Kalman-filter in case the state-vector means are known and we discuss the fundamentally different roles played by the initialization of the two filters. |
| first_indexed | 2025-11-14T08:42:49Z |
| format | Journal Article |
| id | curtin-20.500.11937-35782 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:42:49Z |
| publishDate | 2014 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-357822019-02-19T05:36:13Z A recursive linear MMSE filter for dynamic systems with unknown state vector means Khodabandeh, A. Teunissen, Peter Best linear unbiased estimation (BLUE) BLUE-BLUP recursion Minimummean squared error (MMSE) Kalman filter Best linear unbiased prediction (BLUP) In this contribution we extend Kalman-filter theory by introducing a new recursive linear minimum mean squared error (MMSE) filter for dynamic systems with unknown state-vector means. The recursive filter enables the joint MMSE prediction and estimation of the random state vectors and their unknown means, respectively. We show how the new filter reduces to the Kalman-filter in case the state-vector means are known and we discuss the fundamentally different roles played by the initialization of the two filters. 2014 Journal Article http://hdl.handle.net/20.500.11937/35782 10.1007/s13137-014-0058-0 Springer fulltext |
| spellingShingle | Best linear unbiased estimation (BLUE) BLUE-BLUP recursion Minimummean squared error (MMSE) Kalman filter Best linear unbiased prediction (BLUP) Khodabandeh, A. Teunissen, Peter A recursive linear MMSE filter for dynamic systems with unknown state vector means |
| title | A recursive linear MMSE filter for dynamic systems with unknown state vector means |
| title_full | A recursive linear MMSE filter for dynamic systems with unknown state vector means |
| title_fullStr | A recursive linear MMSE filter for dynamic systems with unknown state vector means |
| title_full_unstemmed | A recursive linear MMSE filter for dynamic systems with unknown state vector means |
| title_short | A recursive linear MMSE filter for dynamic systems with unknown state vector means |
| title_sort | recursive linear mmse filter for dynamic systems with unknown state vector means |
| topic | Best linear unbiased estimation (BLUE) BLUE-BLUP recursion Minimummean squared error (MMSE) Kalman filter Best linear unbiased prediction (BLUP) |
| url | http://hdl.handle.net/20.500.11937/35782 |