The connection between space weather and Single Event Upsets in polar low earth orbit satellites

Space weather is driven and modulated by the activity in the Sun. Space weather events have the potential to inflict critical damage to space systems. Nowadays, space assets are essential in our basic needs, such as communications, cell phone networks, navigation systems, television and internet. Hence, understanding space weather dynamics and its effects on spacecraft is crucial for satellites engineers and satellite operators, in order to prevent and mitigate its impacts.In the last decade our Sun has erupted several times causing dozens of space weather events. Some of these led to satellite malfunctions and outages lasting from mere hours, up to days and weeks. This research is focused on two different space weather events, March 7–8, 2012, and September 6–10, 2017, that occurred during the last ten years and caused satellite anomalies that are related to an increase in the single event upsets rate. Single event upset is a bit flip in a memory device due to high energy particle interaction with the device sensitive volume. During these two periods, Eros B, a low Earth orbiting polar satellite detected an increased rate of single event upsets on two of its processing computers when the high energy proton flux was elevated. On both occasions X-class flares were detected, and the increased single event upsets count rate in Eros B took place only after the 100 MeV protons flux was three orders of magnitude above the background levels. In this research, Israeli satellite anomalies that were detected are first demonstrated.     

A shadow function model based on perspective projection and atmospheric effect for satellites in eclipse

Accurate Solar Radiation Pressure (SRP) modelling is critical for correctly describing the dynamics of satellites. A shadow function is a unitless quantity varying between 0 and 1 to scale the solar radiation flux at a satellite’s location during eclipses. Errors in modelling shadow function lead to inaccuracy in SRP that degrades the orbit quality. Shadow function modelling requires solutions to a geometrical problem (Earth’s oblateness) and a physical problem (atmospheric effects). This study presents a new shadow function model (PPM_atm) which uses a perspective projection based approach to solve the geometrical problem rigorously and a linear function to describe the reduction of solar radiation flux due to atmospheric effects. GRACE (Gravity Recovery And Climate Experiment) satellites carry accelerometers that record variations of non-conservative forces, which reveal the variations of shadow function during eclipses. In this study, the PPM_atm is validated using accelerometer observations of the GRACE-A satellite. Test results show that the PPM_atm is closer to the variations in accelerometer observations than the widely used SECM (Spherical Earth Conical Model). Taking the accelerometer observations derived shadow function as the “truth”, the relative error in PPM_atm is ?0.79% while the SECM 11.07%. The influence of the PPM_atm is also shown in orbit prediction for Galileo satellites. Compared with the SECM, the PPM_atm can reduce the radial orbit error RMS by 5.6?cm over a 7-day prediction. The impacts of the errors in shadow function modelling on the orbit remain to be systematic and should be mitigated in long-term orbit prediction.     

ITRS/GCRS transformation: Uncertainty propagation analysis and short-term modelling of IAU 2006/2000A developments

A study of the uncertainty propagation in ITRS/GCRS transformation is presented in this work. General law of propagation of variances is applied to the ITRS/GCRS transformation matrix, deriving the analytical expressions to compute GCRS position uncertainty. This evaluation is based on EOP uncertainties provided by IERS long-term series and formal uncertainties of ITRS-compatible coordinates. Numerical results for the period 1998–2016 are shown and discussed for ITRS positions in different altitudes and latitudes, providing graphical and numerical insights of the mapping of EOP uncertainties to transformed coordinates.Eventually, an analysis of short-term evolution of the Celestial Intermediate Pole coordinates in the GCRS provided by the IAU2006/2000A precession-nutation model is carried out, in order to assess the feasibility to potentially broadcast these parameters in GNSS navigation message. This approach would facilitate the dissemination of terrestrial-celestial transformation parameters for real time users, given that polar motion and UT1-UTC are already foreseen in GPS interface specification. The results presented in this work confirm the feasibility of this idea.     

动力学补偿受控卫星轨道确定算法(英文)

受控卫星动力学模型中推力加速度的量级远远高于其他摄动的误差量级,观测量主要反映受控卫星动力学模型的误差。本文以跟踪和精确定位空间机动目标为目的,给出基于地面雷达观测,实时估计推力加速度,修正卫星动力学模型的轨道确定算法。通过建立连续推力控制过程变质量动力学模型,给出常推力变加速度满足的运动学微分方程; 建立变加速度估计系统状态方程,和扩展卡尔曼滤波轨道确定算法; 并给出连续推力控制卫星运动状态关于推力加速度的变分运动方程; 实际飞行控制应用表明: 利用地面测量数据,实时估计推力加速度并补偿系统动力学模型,解决了连续受控卫星轨道精确确定问题。     

海洋测高数据定轨方法研究

海洋动力环境卫星已经成为进行全球海洋和环境监测的重要手段,其上所携带的测高仪是用于测量平均海平面、浪高、重力场等海洋环境参数的主要载荷。本文介绍了国内外星载海洋测高仪的发展历程、主要的技术改进以及目前的新动态。根据测高仪的测量原理,分析了测高仪数据中可能存在的误差,并给出了对应的误差修正模型。详细介绍了Jason-1测高数据文件中所涉及的误差修正量和采用的模型。最后,通过仿真数据和实测数据的计算,分析了单用测高仪数据和交叉点数据进行轨道确定所能达到的精度,为未来的实际应用提供一些参考。     

基于Gram-Schmidt过程的判别变量筛选方法

利用Gram-Schmidt过程,在自变量集合中选择对判别分类解释性最强的信息,删除对分类无显著解释作用的信息以及重复解释的信息,并把挑选出来的解释变量集合变换成若干直交变量.一方面实现了判别分析模型中的变量筛选,同时也解决了自变量多重共线条件下的有效建模问题.在选入变量的过程中运用F统计量检验变量的判别作用,更容易被统计应用人员所接受.为了说明所提算法的合理性和有效性,以Fisher判别分析建模为例,通过仿真数据建模取得了合理准确的分析结论.      

有限长Ramanujan-Fourier快速变换及频率估计

近来出现的Ramnujan-Fourier变换(RFT,Ramanujan Fourier Transformation)是以"Ramanujan和"为基向量的算术变换,该变换可提供分数频率分辨力.首先分析了有限长Ramanujan频谱特点,给出了基向量分布情况,推导了该变换的快速算法,比较了有限长RFT与快速傅里叶变换的乘法计算量;其次,给出了利用RFT的递归峰值检测频率估计算法,并分析了RFT的频率分辨率和适用特点,在非高斯噪声条件下,仿真比较了RFT与傅里叶变换对信号进行频率估计的性能,得到在信噪比为-20 dB的非高斯噪声情况下,频率估计的归一化均方误差可以达到 10-3.      

基于机动平台序列图像的空间飞行器定轨技术研究

针对机动观测平台单目光学成像系统的特点,在不能测定目标飞行器位置和速度的前提下,通过对成像系统与空间飞行器空间关系的分析,提出了视平均运动角速度与真平均运动角速度的概念,并构建了关于二者的约束方程,实现了基于测角数据的观测斜距的估计,从而解算出定轨所需的初始状态参数。基于观测斜距估计的轨道确定方法把对空间飞行器的定轨问题,归结为根据图像序列计算目标测角和根据测角数据确定观测斜距,解决了利用空间单目光学成像数据的定轨问题,并以高轨卫星为实例对定轨精度进行了仿真验证。     

基于FastSLAM的绳系机器人同时定位与地图构建算法

绳系式移动机器人可用于极端地形的探测,如陡峭斜坡、松软土壤、高耸悬崖、沟壑等。在运动过程中移动机器人的绳索不可避免地与障碍物接触甚至缠绕。由于绳索与障碍物之间的接触点不相互独立以及机器人模型的非线性特性,经典的FastSLAM框架不适用于绳索机器人的同时定位和地图创建（SLAM）问题。提出基于改进FastSLAM框架的绳系机器人SLAM算法。在该框架中,分别利用无迹滤波和粒子滤波解决接触点位置估计和机器人位姿估计问题,并利用非线性观测模型的无迹变换来简化粒子权重更新。仿真结果表明,该算法可有效地估计接触点位置,同时提高机器人位姿估计性能。     

DORIS系统自主在轨实时定轨的实现与精度分析

国家科技重大专项高分辨率空间对地观测项目的实施,对卫星平台的自主在轨实时定轨提出了新的需求。由法国发展的DORIS实时定轨系统虽已成功应用于多个对地观测平台,但该系统从未公布其原始数据和数据处理的技术细节,因此本文尝试利用DORIS地面主控站提供的ENVISAT标准格式多普勒数据,假设星上自主定轨时采用相同的数据,基于卡尔曼滤波算法实现（仿真）自主实时定轨。计算表明仅考虑简单的非球形引力模型,对于位置误差1km,速度误差1m/s的初始条件,2小时后滤波趋于稳定,滤波精度为十米量级,速度精度为厘米每秒级。为提高滤波计算效率,对坐标系统转换进行简化后,定轨精度仍在十米量级。基于DORIS仿真测量数据滤波计算表明,随着测量精度的提高和每圈观测弧段的增加,滤波计算的精度也会得到有效提高。