基于预报偏差的LEO航天器轨道异常检测

LEO Spacecraft Orbit Anomaly Detection Based on Prediction Dispersion

针对由轨道控制、大气环境、碰撞等因素造成的低轨(LEO)航天器轨道突变问题,提出了一种基于预报偏差的轨道异常检测方法。选择LEO轨道的半长轴和倾角作为特征轨道参数,利用SGP4模型长期项对目标的两行轨道要素(TLE)进行预报得到特征轨道参数的预报值,通过对特征轨道参数的编目数据和预报数据进行平滑后求差得到预报偏差序列,基于马氏距离对预报偏差数据的两个分量进行联合异常检测。对Terra卫星2010年的机动事件分析结果同NASA发布的其机动历史相吻合,表明该方法可以有效地检测航天器轨道异常的次数、时间和类型,可应用于空间目标的监视与空间态势的感知。

A LEO spacecraft The semi-major detection method based orbit anomaly caused by axis and inclination were on prediction dispersion was presented to deal with maneuvers, collisions, and atmospheric environment. selected as orbit characteristic parameters. Prediction parameters. The orbit anomaly was detected through the prediction dispersion data based on Mahalanobis distance. The detection results of Terra in 2010 is consistent with the maneuver history published by NASA, which indicate that the method presented in this paper can detect the times, epoch and type of orbit anomaly and can be widely applied to the space surveillance and space situational awareness.