Adaptive unscented Gaussian likelihood approximation filter
This paper focuses on the update step of Bayesian nonlinear filtering. We first derive the unscented Gaussian likelihood approximation filter (UGLAF), which provides a Gaussian approximation to the likelihood by applying the unscented transformation to the inverse of the measurement function. The UG...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Pergamon Press
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/54227 |
| _version_ | 1848759319159373824 |
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| author | Garcia Fernandez, Angel Morelande, M. Grajal, J. Svensson, L. |
| author_facet | Garcia Fernandez, Angel Morelande, M. Grajal, J. Svensson, L. |
| author_sort | Garcia Fernandez, Angel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper focuses on the update step of Bayesian nonlinear filtering. We first derive the unscented Gaussian likelihood approximation filter (UGLAF), which provides a Gaussian approximation to the likelihood by applying the unscented transformation to the inverse of the measurement function. The UGLAF approximation is accurate in the cases where the unscented Kalman filter (UKF) is not and the other way round. As a result, we propose the adaptive UGLAF (AUGLAF), which selects the best approximation to the posterior (UKF or UGLAF) based on the Kullback-Leibler divergence. This enables AUGLAF to outperform both the UKF and UGLAF. |
| first_indexed | 2025-11-14T09:57:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-54227 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:57:59Z |
| publishDate | 2015 |
| publisher | Pergamon Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-542272017-11-20T07:23:35Z Adaptive unscented Gaussian likelihood approximation filter Garcia Fernandez, Angel Morelande, M. Grajal, J. Svensson, L. This paper focuses on the update step of Bayesian nonlinear filtering. We first derive the unscented Gaussian likelihood approximation filter (UGLAF), which provides a Gaussian approximation to the likelihood by applying the unscented transformation to the inverse of the measurement function. The UGLAF approximation is accurate in the cases where the unscented Kalman filter (UKF) is not and the other way round. As a result, we propose the adaptive UGLAF (AUGLAF), which selects the best approximation to the posterior (UKF or UGLAF) based on the Kullback-Leibler divergence. This enables AUGLAF to outperform both the UKF and UGLAF. 2015 Journal Article http://hdl.handle.net/20.500.11937/54227 10.1016/j.automatica.2015.02.005 Pergamon Press restricted |
| spellingShingle | Garcia Fernandez, Angel Morelande, M. Grajal, J. Svensson, L. Adaptive unscented Gaussian likelihood approximation filter |
| title | Adaptive unscented Gaussian likelihood approximation filter |
| title_full | Adaptive unscented Gaussian likelihood approximation filter |
| title_fullStr | Adaptive unscented Gaussian likelihood approximation filter |
| title_full_unstemmed | Adaptive unscented Gaussian likelihood approximation filter |
| title_short | Adaptive unscented Gaussian likelihood approximation filter |
| title_sort | adaptive unscented gaussian likelihood approximation filter |
| url | http://hdl.handle.net/20.500.11937/54227 |