Kullback–Leibler divergence approach to partitioned update Kalman filter
Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are applied first. In this paper, we generalize partitioned update Kalman filter, which requires the use of...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier BV
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/54513 |
| _version_ | 1848759390497144832 |
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| author | Raitoharju, M. Garcia Fernandez, Angel Piché, R. |
| author_facet | Raitoharju, M. Garcia Fernandez, Angel Piché, R. |
| author_sort | Raitoharju, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are applied first. In this paper, we generalize partitioned update Kalman filter, which requires the use of the second order extended Kalman filter, so that it can be used with any Kalman filter extension such as the unscented Kalman filter. To do so, we use a Kullback–Leibler divergence approach to measure the nonlinearity of the measurement, which is theoretically more sound than the nonlinearity measure used in the original partitioned update Kalman filter. Results show that the use of the proposed partitioned update filter improves the estimation accuracy. |
| first_indexed | 2025-11-14T09:59:07Z |
| format | Journal Article |
| id | curtin-20.500.11937-54513 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:59:07Z |
| publishDate | 2017 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-545132018-03-29T09:09:36Z Kullback–Leibler divergence approach to partitioned update Kalman filter Raitoharju, M. Garcia Fernandez, Angel Piché, R. Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are applied first. In this paper, we generalize partitioned update Kalman filter, which requires the use of the second order extended Kalman filter, so that it can be used with any Kalman filter extension such as the unscented Kalman filter. To do so, we use a Kullback–Leibler divergence approach to measure the nonlinearity of the measurement, which is theoretically more sound than the nonlinearity measure used in the original partitioned update Kalman filter. Results show that the use of the proposed partitioned update filter improves the estimation accuracy. 2017 Journal Article http://hdl.handle.net/20.500.11937/54513 10.1016/j.sigpro.2016.07.007 Elsevier BV restricted |
| spellingShingle | Raitoharju, M. Garcia Fernandez, Angel Piché, R. Kullback–Leibler divergence approach to partitioned update Kalman filter |
| title | Kullback–Leibler divergence approach to partitioned update Kalman filter |
| title_full | Kullback–Leibler divergence approach to partitioned update Kalman filter |
| title_fullStr | Kullback–Leibler divergence approach to partitioned update Kalman filter |
| title_full_unstemmed | Kullback–Leibler divergence approach to partitioned update Kalman filter |
| title_short | Kullback–Leibler divergence approach to partitioned update Kalman filter |
| title_sort | kullback–leibler divergence approach to partitioned update kalman filter |
| url | http://hdl.handle.net/20.500.11937/54513 |