A Multiplicative Residual Approach To Attitude Kalman Filtering With Unit-Vector Measurements
A Multiplicative Residual Approach To Attitude Kalman Filtering With Unit-Vector Measurements
- New search for: Zanetti, Renato
- New search for: Zanetti, Renato
Abstract Using direction vectors of unit length as measurements for attitude estimation in an extended Kalman filter inevitably results in a singular measurement covariance matrix. Singularity of the measurement covariance means no noise is present in one component of the measurement. Unit-vector measurements have no noise in the radial component. Singular measurement covariances can be dealt with by the classic Kalman filter formulation as long as the estimated measurement covariance is non-singular in the same direction. Unit-vector measurements violate this condition because both the true measurement and the estimated measurement have perfectly known lengths. Minimum variance estimation for the unit-vector attitude Kalman filter is studied in this work. An optimal multiplicative residual approach is presented. The proposed approach is compared with the classic additive residual attitude Kalman filter.
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A Multiplicative Residual Approach To Attitude Kalman Filtering With Unit-Vector Measurements
The Journal of the Astronautical Sciences ; 57 , 4 ; 793-801
2009-10-01
9 pages
Article (Journal)
Electronic Resource
English
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