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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。
Aiming at the orbit mutation of LEO spacecraft due to orbital control, atmospheric environment and collision, an orbit anomaly detection method based on forecast bias is proposed. The semi-major axis and dip angle of LEO orbit are selected as the characteristic orbit parameters, and the long-term term of SGP4 model is used to predict the two-element orbit elements (TLE) of the target to obtain the predicted values of the characteristic orbital parameters. Based on the cataloging and forecasting data of the characteristic orbital parameters After the smoothing, the forecasting deviation sequence is obtained and the joint anomaly detection is performed on the two components of the forecast deviation data based on the Mahalanobis distance. The results of the maneuver analysis of Terra satellite in 2010 coincide with the maneuver histories issued by NASA, indicating that this method can effectively detect the number, time and type of spacecraft orbital anomalies and can be applied to the monitoring of space targets and the perception of the spatial situation .