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New Approach for the Evolution and Expansion of Space Debris Scenario

机译：空间碎片场景演化和扩展的新方法

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摘要

The evolution of the space debris scenario consisting of a very large number of fragments is described using the propagation of the characteristics of equivalent fragments without propagating each and every individual debris fragment. This is similar to characterizing a fluid in terms of the average density, pressure, and temperature without considering the velocities of individual molecules in a fluid element. The space debris fragments are assigned to a three-dimensional bin of semimajor axis, eccentricity, and ballistic coefficient. A suitably defined representative semimajor axis, eccentricity, and an equivalent ballistic coefficient (a.e.B) are defined for the equivalent fragments in each of the bins. A constant gain Kalman filtering technique based on 1) propagating the above characteristics, and 2) updating them as and when further measurements become available, has been proposed. Further, the assimilation of the information from other breakups with the passage of time is also possible. The robustness of the constant Kalman gain approach instead of using the Kahnan filter statistics helps to handle better the unmodeled or unmodelable errors due to the finite bin size and the environmental perturbations. This methodology is also suggested to handle massive atmospheric data assimilation problems.
机译：在不传播每个碎片的情况下，通过传播等效碎片的特征来描述由大量碎片组成的空间碎片场景的演变。这类似于根据平均密度，压力和温度来表征流体，而不考虑流体元件中单个分子的速度。将空间碎片碎片分配给半长轴，偏心率和弹道系数的三维区间。为每个仓中的等效碎片定义了适当定义的代表性半长轴，偏心率和等效弹道系数（a.e.B）。已经提出了一种恒定增益卡尔曼滤波技术，该技术基于1）传播上述特性，以及2）在进一步测量可用时更新它们。此外，随着时间的流逝，来自其他分手的信息同化也是可能的。恒定卡尔曼增益方法的健壮性而不是使用Kahnan滤波器统计信息，有助于更好地处理由于有限的bin大小和环境扰动而引起的无法建模或无法建模的误差。还建议使用此方法来处理大量的大气数据同化问题。

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