The amount of man-made debris is sharply increasing because of the increase in human activities being carried out in space. This could seriously threaten spacecraft performance and even lead to system instability. In order to guarantee the safe operation of spacecraft, it is necessary to make an in-depth investigation on debris clouds produced by different hypervelocity impact (HVI) experiments and make an accurate damage estimation based on debris characteristics. The interest of this article is to discuss the main properties of debris clouds that play a central role in damage estimation of the rear wall of the Whipple shield protective structure. Those properties include the impact velocity, the evolution velocity, the diameter of the projectile, and the incident angle of debris to the rear wall. The purpose of this article is to track debris fragments and construct a simple trajectory model by employing 2-D image processing techniques, and make a damage estimation of debris clouds through different experimental results comparison. More specifically, by using the mean shift algorithm, image noise is greatly eliminated while reserving edges. Then, in terms of the $K$-means algorithm, the overlapping that occurs in some frames is mostly handled and fragments of the underlying images are successfully segmented. Moreover, by utilizing the scale invariant feature transform technique, the image matching is fulfilled, and the trajectory is further established. Finally, based on the established trajectory, the comparison of debris clouds generated by different HVI experiments is provided, through which an accurate damage estimation is formulated. Experimental results demonstrate the effectiveness and validity of the proposed algorithm via image processing.


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    Title :

    A Tracking Algorithm for Debris Cloud Fragments Produced by Different Hypervelocity Impacts Through Image Processing


    Contributors:
    Tian, Xin (author) / Wang, Xiao (author) / Song, Yan (author) / Wei, Guoliang (author) / Zeng, Ru (author)


    Publication date :

    2023-10-01


    Size :

    7539661 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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