Several candidate methods for classifying an agile space-object active control mode are tested using simulated light curve data and a Rao–Blackwellized particle filter. The first measure to discriminate the space-object control mode is a measurement dissimilarity metric, defined as the time integral of the error between the estimated target space-object sensor boresight and the line-of-sight vector to each hypothesized subject. The second measure quantifies the “pointing quality” using the multivariate Gaussian mixture model analog to the Mahalanobis distance, which is computed using the estimated multivariate body angular velocity distributions. It is shown how additional information from the space-object shape model can be combined with radiometric first principles to establish a tracking error threshold between the target space object and the hypothesized subject. Finally, the body angular velocity estimates are used to compute the mass-specific rotational angular momentum and mass-specific rotational kinetic-energy analogs. These analogs are coupled with statistical inference techniques to classify the active control modes of agile space objects.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Space-Object Active Control Mode Inference Using Light Curve Inversion


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.01.2018




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Shape Identification of Space Objects via Light Curve Inversion Using Deep Learning Models

    Furfaro, Roberto | British Library Conference Proceedings | 2019


    Space Object Shape Characterization and Tracking Using Light Curve and Angles Data

    Linares, Richard / Jah, Moriba K. / Crassidis, John L. et al. | AIAA | 2014


    Light Curve Inversion Observability Analysis (AAS 19-788)

    Friedman, Alex M. / Fan, Siwei / Frueh, Carolin | TIBKAT | 2020