Robust State Estimation with Sparse Outliers
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JGCD_Graham_How_Gustafson_2015.pdf
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619.74 KB
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3234229e9cc126f41724528fae18f338
Author(s)
Graham, Matthew C.
How, Jonathan P
Gustafson, Donald E.
Date Issued
July 2015
Journal
Journal of Guidance, Control, and Dynamics
Publisher
American Institute of Aeronautics and Astronautics
Citation
Graham, Matthew C., Jonathan P. How, and Donald E. Gustafson. “Robust State Estimation with Sparse Outliers.” Journal of Guidance, Control, and Dynamics 38.7 (2015): 1229–1240.
Version
Author's final manuscript
Abstract
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 filtering algorithms that can address measurement outliers, in general, they cannot provide robust state estimates when state propagation outliers occur. This paper presents a robust recursive filtering algorithm, the l1l1-norm filter, which can provide reliable state estimates in the presence of both measurement and state propagation outliers. In addition, Monte Carlo simulations and vision-aided navigation experiments demonstrate that the proposed algorithm can provide improved state estimation performance over existing robust filtering approaches.
MIT Department
Charles Stark Draper Laboratory
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.2514/1.g000350
