A new acceleration characterization filter is formulated for reentry orbit determination with unmodeled maneuvers. Drag and lift accelerations are treated as deterministic disturbances that can be characterized statistically and included in covariance predictions. Mean-square acceleration covariances are determined by a statistical analysis of expected maneuvers. Adaptation to the dynamic reentry environment is improved because the mean-square acceleration covariance depends on dynamic pressure. Performance simulations demonstrate the accuracy and effectiveness of this adaptive filter for demanding reentry maneuvers. Monte Carlo techniques assess accuracy sensitivity to modeling assumptions and to offnominal trajectories.


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    Titel :

    Acceleration Characterization for Reentry Orbit Determination with Unmodeled Maneuvers


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2018-02-26


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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