Two Kalman-filter formulations are presented for the estimation of spacecraft sensor misalignments from inflight data. In the first the sensor misalignments are part of the filter state variable; in the second, which we call HYLIGN, the state vector contains only dynamical variables, but the sensitivities of the filter innovations to the misalignments are calculated within the Kaiman filter. This procedure permits the misalignments to be estimated in batch mode as well as a much smaller dimension for the Kaiman filter state vector. This results not only in a significantly smaller computational burden but also in a smaller sensitivity of the misalignment estimates to outliers in the data. Numerical simulations of the filter performance are presented.


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    Title :

    Batch, Sequential and Hybrid Approaches to Spacecraft Sensor Alignment Estimation


    Additional title:

    J of Astronaut Sci


    Contributors:

    Published in:

    Publication date :

    2003-09-01


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Batch, Sequential and Hybrid Approaches to Spacecraft Sensor Alignment Estimation

    Zanardi, Maria Celília / Shuster, Malcolm D. | Online Contents | 2003



    Batch and filter approaches to spacecraft sensor alignment estimation

    Zanardi, M. C. / Shuster, M. D. / European Space Agency | British Library Conference Proceedings | 1997


    Batch and filter approaches to spacecraft sensor alignment estimation

    Zanardi, M. C. / Shuster, M. D. | British Library Online Contents | 1997


    Spacecraft alignment estimation

    Bierman, G.J. / Shuster, M.D. | Tema Archive | 1988