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- W3208420444 abstract "This letter proposes a novel stochastic nonlinear neural-adaptive-based filter on <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$SO(3)$ </tex-math></inline-formula> for the attitude estimation problem. The proposed filter produces good results given measurements extracted from low-cost sensing units (e.g., IMU or MARG sensor modules). The filter is guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. In addition to Lie Group formulation, quaternion representation of the proposed filter is provided. The effectiveness of the proposed neural-adaptive filter is tested and evaluated in its discrete form under the conditions of large initialization error and high measurement uncertainties." @default.
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- W3208420444 date "2022-01-01" @default.
- W3208420444 modified "2023-10-18" @default.
- W3208420444 title "Neural-Adaptive Stochastic Attitude Filter on SO(3)" @default.
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- W3208420444 doi "https://doi.org/10.1109/lcsys.2021.3123227" @default.
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