Two recursive estimation algorithms, which use pairs of measured vectors to yield minimum variance estimates of the quaternion of rotation, are presented. The nonlinear relations between the direction cosine matrix and the quaternion are linearized, and a variant of the extended Kalman filter is used to estimate the difference between the quaternion and its estimate. With each measurement this estimate is updated and added to the whole quaternion estimate. This operation constitutes a full state reset in the estimation process. Filter tuning is needed to obtain a converging filter. The second algorithm presented uses the normality property of the quaternion of rotation to obtain, in a straightforward design, a filter which converges, with a smaller error, to a normal quaternion. This algorithm changes the state but not the covariance computation of the original algorithm and implies only a partial reset. Results of Monte-Carlo simulation runs are presented which demonstrate the superiority of the normalized quaternion.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Attitude Determination from Vector Observations: Quaternion Estimation


    Contributors:

    Published in:

    Publication date :

    1985-01-01


    Size :

    1619300 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Attitude determination from vector observations: quaternion estimation

    Bar-Itzhack, I.Y. / Oshman, Y. | Tema Archive | 1985



    Attitude Quaternion Estimation from Two Vector Observations

    Choukroun, Daniel / Peng, Caitong | AIAA | 2025


    Quaternion Attitude Estimation Using Vector Observations

    Markley, F. Landis / Mortari, Daniele | Springer Verlag | 2000


    Quaternion Attitude Estimation Using Vector Observations

    Markley / Mortari / American Astronautical Society | British Library Conference Proceedings | 2000