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  • A 2-dimensional Stochastic Model Of Space Debris Environment

    Paper ID

    7609

    author

    • Canan Li
    • Baojun Pang
    • Wei Zhang
    • Li Ding

    company

    Harbin Institute of Technology

    country

    China

    year

    2007

    abstract

    The risk of spacecrafts colliding with space debris is getting increasing concern. In such collision risk analysis, the basic tool is the space debris environment model. Currently some kinds of models are popular internationally, i.e., ORDEM, MASTER, SDPA, etc.. This paper is devoted to introduce the theoretical foundation of a stochastic model, which is developed to improve the efficiency and accuracy of stochastic models. Since a one-dimensional description and treatment in altitude of the debris environment is not adequate to address all issues, and since a three-dimensional model seems to be too much because of the argument of perigee's nearly uniformly distribution, a two-dimensional(altitude and latitude) model is developed. The basic data of the model is the historic launch data and the historic fragmentation data. To avoid the time-taking procedure of propagating large population of space debris, space debris is classified according to their perigee altitude, apogee altitude and eccentricity. After classification, only the information of the representatives of each class and the distributions within each class is kept. By applying propagation on the representatives and regenerate the population of each class, the propagation of the whole population of space debris is completed, while the time cost by which is greatly reduced. Also presented in the output of the model is the mass information of the space debris, which is an important factor in collision risk analysis and is unavailable even in the TLE data set. The justification of the method is shown with theoretical analysis. The model based on the theory described in this paper can be run on a single PC, which is unlike some revolutionary models that takes many CPU units to prepare the data for further calculation. A comparison with the TLE data set is also presented, which furthers the validation of the method.