Robust State Estimation with Sparse Outliers
One of the major challenges for state estimation algorithms, such as the Kalman filter, is the impact of outliers that do not match the assumed process and measurement noise. When these errors occur, they can induce large state estimate errors and even filter divergence. Although there are robust fi...
| Huvudupphovsmän: | , , |
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| Materialtyp: | Artikel |
| Språk: | en_US |
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American Institute of Aeronautics and Astronautics
2016
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| Länkar: | http://hdl.handle.net/1721.1/105810 https://orcid.org/0000-0001-8576-1930 |