Nonlinear filtering update phase via the single point truncated unscented Kalman filter
A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modifie...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/53626 |
| _version_ | 1848759188337983488 |
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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 | A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kahnan filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter. © 2011 IEEE. |
| first_indexed | 2025-11-14T09:55:55Z |
| format | Conference Paper |
| id | curtin-20.500.11937-53626 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:55:55Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-536262017-06-23T03:00:42Z Nonlinear filtering update phase via the single point truncated unscented Kalman filter Garcia Fernandez, Angel Morelande, M. Grajal, J. A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kahnan filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter. © 2011 IEEE. 2011 Conference Paper http://hdl.handle.net/20.500.11937/53626 restricted |
| spellingShingle | Garcia Fernandez, Angel Morelande, M. Grajal, J. Nonlinear filtering update phase via the single point truncated unscented Kalman filter |
| title | Nonlinear filtering update phase via the single point truncated unscented Kalman filter |
| title_full | Nonlinear filtering update phase via the single point truncated unscented Kalman filter |
| title_fullStr | Nonlinear filtering update phase via the single point truncated unscented Kalman filter |
| title_full_unstemmed | Nonlinear filtering update phase via the single point truncated unscented Kalman filter |
| title_short | Nonlinear filtering update phase via the single point truncated unscented Kalman filter |
| title_sort | nonlinear filtering update phase via the single point truncated unscented kalman filter |
| url | http://hdl.handle.net/20.500.11937/53626 |