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空间碎片编目中的无法编目物体的再次关联及航天器与碎片碰撞预警过程中都可能用到碎片的协方差信息。碎片协方差信息包括轨道初始误差、测量设备误差以及摄动运动方程的模型误差等。如何科学合理地对空间碎片的协方差做出演化估计,对提高空间目标编目效率以及改善空间碎片预警精度有重要作用。分析线性协方差演化方法在低轨道空间目标国际空间站和AJISAI卫星上的应用,并把卡尔曼滤波方法应用到空间碎片协方差演化过程中。通过无迹卡尔曼滤波(Unscent Kalman Filter,UKF)中的UT转换来对未来协方差进行sigma点估计。仿真分析表明200 min演化时间,UKF协方差演化方法可以提高国际空间站协方差演化精度,而对于AJISAI卫星线性方法和UKF方法协方差演化结果基本相等。最后通过蒙特卡洛方法统计分析了10个采样点的预报协方差,验证了两种方法的准确性。
The re-association of uncatalogable objects in the space debris catalog and the covariance information for the debris may be used during spacecraft and debris collision warnings. Fragment covariance information includes initial orbital errors, measurement equipment errors, and model errors of perturbed motion equations. How to estimate the evolutionary covariance of space debris scientifically and rationally plays an important role in improving the efficiency of space object cataloging and improving the accuracy of early warning of space debris. This paper analyzes the application of linear covariance evolution method in low orbiting space target ISSA and AJISAI satellite and applies Kalman filter to the evolution of space debris covariance. Future covariances are sigma-point-estimated through UT conversions in Unscented Kalman Filter (UKF). The simulation results show that evolutionary time of 200 min and UKF covariance evolution method can improve the evolutionary precision of covariance of ISS. The results of covariance evolution are basically the same for AJISAI satellite method and UKF method. Finally, the forecast covariance of 10 sampling points was statistically analyzed by Monte Carlo method, which verified the accuracy of the two methods.