Abstract This paper presents a new method to determine the mass of an inactive space object from the fusion of photometric and astrometric data. Typically, the effect of solar radiation pressure is used to determine area-to-mass ratio for space objects from angles observations. The area-to-mass ratio of a space object can greatly affect its orbital dynamics. As a consequence, angles data are sensitive to this quantity. On the other hand, photometric data is not sensitive to mass but is a strong function of the albedo-area and the rotational dynamics of the space object. The albedo-area can be used to determine the amount of energy reflected from solar radiation. Since these two data types are sensitive to albedo-area and area-to-mass, then through fusion of photometric data with angles data it is possible to determine the area and mass of a space object. This work employs an unscented Kalman filter to estimate rotational and translational states, area and mass of an inactive space object. Mass is not observable with only angles data or only photometric data alone, but it is shown in this work that with the two combined data types mass can be recovered. Recovery of space object characteristics and attitude and orbit trajectories with sufficient accuracy is demonstrated in this paper via simulation.

    Highlights New method to determine the mass from the fusion of photometric and astrometric data. Employs an UKF to estimate rotational and translational states, area and mass. Mass can be recovered with combined photometric and astrometric data.


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    Titel :

    Astrometric and photometric data fusion for inactive space object mass and area estimation


    Beteiligte:
    Linares, Richard (Autor:in) / Jah, Moriba K. (Autor:in) / Crassidis, John L. (Autor:in) / Leve, Fred A. (Autor:in) / Kelecy, Tom (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2013-10-24


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch