Over the last several decades, the growing amount of space debris has increased the hazard of in-orbit collision between operational satellites and space debris. As the impact with small fragments could partially or completely damage a satellite, an effective method to quickly quantify the risk posed by space debris is required to systemically preserve the mission and prevent the creation of new fragments. In contrast to the traditional approach that calculates the probability of collision by representing the debris cloud as a continuous debris density function, this work focuses on developing a tool to quickly assess the collision risk directly from a random, discrete debris population where each fragment has a specified orbit. The proposed tool makes use of the following. First, a sequence of filters was used to detect any close encounters between the debris cloud population and the satellite, hence reducing the debris population to a shortlist of possible objects within range of collision. Secondly, an advanced stochastic technique known as Subset Simulation, and which employs the Markov Chain Monte Carlo Method, was used to calculate the probability of collision for the debris cloud. The validity of the proposed tool was confirmed by estimating the collision probability using a test case from the literature. In addition, the performance of the tool in terms of the computational time was also assessed.


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    Title :

    Probability of collision for a newly generated debris cloud using subset simulation technique



    Conference:

    7TH INTERNATIONAL SEMINAR ON AEROSPACE SCIENCE AND TECHNOLOGY – ISAST 2019 ; 2019 ; Jakarta, Indonesia


    Published in:

    Publication date :

    2020-04-21


    Size :

    10 pages





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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