Outlier rejection methods for robust Kalman filtering
In this paper we discuss efficient methods of the state estimation which are robust against unknown outlier measurements. Unlike existing Kalman filters, we relax the Gaussian assumption of noises to allow sparse outliers. By doing so spikes in channels, sensor failures, or intentional jamming can b...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/56257 |
| _version_ | 1848759827062325248 |
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| author | Kim, Du Yong Lee, S. Jeon, M. |
| author_facet | Kim, Du Yong Lee, S. Jeon, M. |
| author_sort | Kim, Du Yong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we discuss efficient methods of the state estimation which are robust against unknown outlier measurements. Unlike existing Kalman filters, we relax the Gaussian assumption of noises to allow sparse outliers. By doing so spikes in channels, sensor failures, or intentional jamming can be effectively avoided in practical applications. Two approaches are suggested: median absolute deviation (MAD) and L 1 -norm regularized least squares (L 1 -LS). Through a numerical example two methods are tested and compared. © 2011 Springer-Verlag. |
| first_indexed | 2025-11-14T10:06:04Z |
| format | Conference Paper |
| id | curtin-20.500.11937-56257 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:06:04Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-562572017-09-13T16:11:34Z Outlier rejection methods for robust Kalman filtering Kim, Du Yong Lee, S. Jeon, M. In this paper we discuss efficient methods of the state estimation which are robust against unknown outlier measurements. Unlike existing Kalman filters, we relax the Gaussian assumption of noises to allow sparse outliers. By doing so spikes in channels, sensor failures, or intentional jamming can be effectively avoided in practical applications. Two approaches are suggested: median absolute deviation (MAD) and L 1 -norm regularized least squares (L 1 -LS). Through a numerical example two methods are tested and compared. © 2011 Springer-Verlag. 2011 Conference Paper http://hdl.handle.net/20.500.11937/56257 10.1007/978-3-642-22333-4_41 restricted |
| spellingShingle | Kim, Du Yong Lee, S. Jeon, M. Outlier rejection methods for robust Kalman filtering |
| title | Outlier rejection methods for robust Kalman filtering |
| title_full | Outlier rejection methods for robust Kalman filtering |
| title_fullStr | Outlier rejection methods for robust Kalman filtering |
| title_full_unstemmed | Outlier rejection methods for robust Kalman filtering |
| title_short | Outlier rejection methods for robust Kalman filtering |
| title_sort | outlier rejection methods for robust kalman filtering |
| url | http://hdl.handle.net/20.500.11937/56257 |