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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
American Institute of Aeronautics and Astronautics
2016
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| Online Access: | http://hdl.handle.net/1721.1/105810 https://orcid.org/0000-0001-8576-1930 |