Mixture truncated unscented Kalman filtering
This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified p...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/63304 |
| _version_ | 1848761050646708224 |
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| author | Garcia Fernandez, Angel Morelande, M. Grajal, J. |
| author_facet | Garcia Fernandez, Angel Morelande, M. Grajal, J. |
| author_sort | Garcia Fernandez, Angel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary. © 2012 ISIF (Intl Society of Information Fusi). |
| first_indexed | 2025-11-14T10:25:31Z |
| format | Conference Paper |
| id | curtin-20.500.11937-63304 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:25:31Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-633042018-02-06T06:16:27Z Mixture truncated unscented Kalman filtering Garcia Fernandez, Angel Morelande, M. Grajal, J. This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary. © 2012 ISIF (Intl Society of Information Fusi). 2012 Conference Paper http://hdl.handle.net/20.500.11937/63304 restricted |
| spellingShingle | Garcia Fernandez, Angel Morelande, M. Grajal, J. Mixture truncated unscented Kalman filtering |
| title | Mixture truncated unscented Kalman filtering |
| title_full | Mixture truncated unscented Kalman filtering |
| title_fullStr | Mixture truncated unscented Kalman filtering |
| title_full_unstemmed | Mixture truncated unscented Kalman filtering |
| title_short | Mixture truncated unscented Kalman filtering |
| title_sort | mixture truncated unscented kalman filtering |
| url | http://hdl.handle.net/20.500.11937/63304 |