Abstract For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).

    Highlights The novel unscented predictive variable structure filter (UPVSF) is presented with sufficient theoretical proofs. The UPVSF is proposed based on the PVSF and UT sampling strategy. The UKF is employed to compare with the proposed two estimate methods. Simulation results show that the proposed two methods have better estimation performance than the UKF.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors


    Beteiligte:
    Cao, Lu (Autor:in) / Li, Hengnian (Autor:in)

    Erschienen in:

    Acta Astronautica ; 127 ; 505-513


    Erscheinungsdatum :

    2016-06-23


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch