全球星摄动运动及摄动补偿运控策略研究

绕地卫星摄动运动是导致星座设计构型发散的主要因素,摄动补偿轨道控制是维持全     

ESA’s process for the identification and assessment of high-risk conjunction events

ESA’s Space Debris Office provides an operational service for the assessment of collision risks of ESA satellites. Currently, the ENVISAT and ERS-2 missions in low Earth orbits are covered by this service. If an upcoming high-risk conjunction event is predicted based on analysis of Two-Line Element (TLE) data from the US Space Surveillance Network, then independent tracking data of the potential high-risk conjunction object are acquired to improve the knowledge of its orbit. This improved knowledge and the associated small error covariances derived from the orbit determination process scale down the position error ellipsoid at the conjunction epoch. Hence, for the same miss-distance, in most cases an avoidance manoeuvre can be suppressed with an acceptable residual risk.     

风云四号卫星双程测距系统精密轨道确定

针对我国新一代静止轨道卫星风云四号高精度轨道计算需求,地面跟踪系统设计了多台站双频双程测距模式。详细给出了信号传播介质改正中的电离层与对流层处理方法,并给出了风云四号卫星动力学轨道确定策略。在非变轨期间,采用动力学定轨方法。轨道确定残差分析,测量噪声均方根优于0.5 m。通过轨道重叠分析,非变轨期间精度优于20 m。动量轮卸载期间,采用估计经验力的方法,其定轨残差优于1 m。对多弧段数据处理表明文中方法满足同步卫星双程测距模式下的高精度轨道跟踪问题。     

Astrometric positioning and orbit determination of geostationary satellites

In the project titled “Astrometric Positioning of Geostationary Satellite” (PASAGE), carried out by the Real Instituto y Observatorio de la Armada (ROA), optical observation techniques were developed to allow satellites to be located in the geostationary ring with angular accuracies of up to a few tenths of an arcsec. These techniques do not necessarily require the use of large telescopes or especially dark areas, and furthermore, because optical observation is a passive method, they could be directly applicable to the detection and monitoring of passive objects such as space debris in the geostationary ring.     

Application of CCD drift-scan photoelectric technique on monitoring GEO satellites

Geosynchronous Earth Orbit (GEO) satellites are widely used because of their unique characteristics of high-orbit and remaining permanently in the same area of the sky. Precise monitoring of GEO satellites can provide a key reference for the judgment of satellite operation status, the capture and identification of targets, and the analysis of collision warning. The observation using ground-based optical telescopes plays an important role in the field of monitoring GEO targets. Different from distant celestial bodies, there is a relative movement between the GEO target and the background reference stars, which makes the conventional observation method limited for long focal length telescopes. CCD drift-scan photoelectric technique is applied on monitoring GEO targets. In the case of parking the telescope, the good round images of the background reference stars and the GEO target at the same sky region can be obtained through the alternating observation of CCD drift-scan mode and CCD stare mode, so as to improve the precision of celestial positioning for the GEO target. Observation experiments of GEO targets were carried out with 1.56-meter telescope of Shanghai Astronomical Observatory. The results show that the application of CCD drift-scan photoelectric technique makes the precision of observing the GEO target reach the level of 0.2″, which gives full play to the advantage of the long focal length of the telescope. The effect of orbit improvement based on multi-pass of observations is obvious and the prediction precision of extrapolating to 72-h is in the order of several arc seconds in azimuth and elevation.     

Improving orbit prediction accuracy through supervised machine learning

Due to the lack of information such as the space environment condition and resident space objects’ (RSOs’) body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs’ trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO’s orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.     

基于J2项摄动的MMS任务编队优化设计

Magnetospheric MultiScale（MMS）任务利用椭圆轨道远地点附近的正四面体航天器编队，协同完成对地球磁层结构和动力学特性的测量和分析。采用基于轨道根数的相对运动模型，分析了主航天器轨道根数对J2项影响下四面体平均性能指标——质量因子均值和平均边长均值的影响规律，并由此提出一种编队轨道优化设计方案，将其应用于第1阶段MMS任务的四面体构形设计中。该方案的设计变量包括主航天器的6个轨道根数和3个从航天器的15个相对轨道根数（除相对半长轴外），目标函数既考虑到四面体编队的平均性能，又兼顾了3个从航天器相对运动的受摄影响。仿真算例显示，在不施加主动控制的条件下，利用该方案设计远地点附近平均性能保持最优的四面体编队是可行的。     

卫星总体参数对SAR分辨特性影响分析及仿真研究

建立了星载合成孔径雷达(SAR)分辨特性的数学表达式，分析了地球自转和地球扁率、轨道摄动、卫星姿态指向误差和稳定度以及星载SAR的中心视角对星载SAR分辨特性的影响，并进行了数字仿真。分析仿真结果表明，滚动向指向误差和稳定度以及星载SAR中心视角对分辨特性影响较大，该研究结果对于卫星总体参数确定和优化设计具有参考价值。     

利用小波分析识别空间目标的轨道机动

空间目标的轨道机动是与机械能的变化联系在一起的。机械能无法直接测量，只能通过空间目标的位置与速度计算。通过地面雷达可以得到空间目标的测距与测角信息，可以将其转为位置信息。空间目标的速度也不易直接测量，需要根据位置信息，通过微分平滑处理获得。由于测量噪声的影响，机动引起的机械能的变化淹没在噪声中，不容易识别。小波分析可以在一定程度上将淹没在噪声中的机械能变化揭示出来，提高轨道机动的识别能力。利用二进小波在不同尺度下分析机械能随时间的变化，尺度由小至大时真实信号含量增加，噪声含量受到抑制。观察小波系数曲线随小波分析尺度的变化趋势就可以快速判定是否存在轨道机动。仿真结果验证了识别方法的可行性。     

近圆轨道编队飞行星座相对构型稳定性分析

在近圆轨道编队飞行的假设条件下 ,根据动力学关系推导出了环绕卫星相对参考卫星的运动学简化模型 ,并以此简化模型为基础 ,研究了半长轴入轨偏差δa ,轨道倾角偏差δi ,轨道偏心率偏差δe在地球扁率摄动条件下对编队飞行星座相对构型稳定性影响 ,深入分析了各种因素的规律和特点 ,并以此对编队飞行星座轨道设计提出建议。