Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data

Attitude estimation is a critical component of the Attitude Determination and Control System (ADCS) of any satellite. It is used to convert the sensor observation data to an estimated attitude using filtering algorithms. However, in the presence of sensor faults, the ADCS fails to achieve the desired attitude accuracy. In this paper, the Fault Tolerant Extended Kalman Filter (FTEKF) is proposed to handle this imperfection. In accordance, various filtering steps are included in the FTEKF design to enhance both attitude estimation and sensor fault detection. The developed algorithm can detect and isolate any unexpected sensor faults in real time, which provides a reliable attitude estimation. A comparative study with the classical and robust Kalman filters is performed through numerical simulations in order to validate the effectiveness of the adopted filter in case of magnetometer fault data.     

地基光学图像中本体-帆板结构航天器姿态估计方法

提出一种利用地基光学图像估计空间目标姿态的方法。针对本体-帆板结构航天器外形结构特点，定义了一种不易遮挡的两轴结构特征，设计了地基光学图像中该特征的提取方法；根据航天器成像过程建立了特征点二三维几何投影方程，实现了航天器三维姿态的解算。利用单站序列图像，可获得航天器姿态变化角速度信息。在轨航天器的试验结果表明，本方法能有效地从地基光学图像估计出航天器姿态。     

Space debris cumulative flux considering the Interval Distance-based method

The availability of engineering models to estimate the risk from space debris is essential for space missions. According to current research, cumulative flux calculation is mostly carried out based on the equal-width interval discretization. The method discretizes the volume around the Earth into cells defined in earth centered inertial coordinates. The resulting debris flux onto a target object is shown to depend on the chosen size of the cells. To avoid a discretization error, this must be accounted for. In order to present reliable flux predictions for space mission, the algorithm improvement is an ongoing topic for the related research field. The aim of this study was to examine the discretization error during the cumulative flux determination process. Both the effect of interval step length and the orbital boundary are under investigation. Several typical orbits are selected as examples here and the 2018/01/03 TLE (Two Line Element) data published by the US Space Surveillance Network is used as the debris background in this paper. Furthermore, the Interval Distance-Based method for Discretization (IDD) is adopted in this paper. A position-centered flux determination method is introduced based on the IDD method. According to the example analysis, the IDD used in the flux calculation process provides results which are less affected by the interval step-size setup; and the orbital boundary has no effect on the calculation process. In other words, the discretization error is significantly reduced. The position-centered method provided a possible suggestion for the improvement of space debris environment models.     

State and disturbance estimation for test masses of drag-free satellites based on self-recurrent wavelet neural network

In recent years, the drag-free satellites have been widely used for some fundamental physical experiments, such as checking short-range effects of general relativity, geopotential determination and the exploration of static ocean current. And the space-borne detector of gravitational waves is one of the important applications for drag-free satellites in the future. In this study, the estimation of relative motion state and disturbance for test masses of drag-free satellite after release are researched. Firstly, the relative motion model between the test masses is established based on corresponding reference frames. Secondly, a self-recurrent wavelet neural network estimator is designed to estimate the pure gravitational relative motion state and disturbance for the test masses, and the sliding mode controller is used to transfer the relative motion state to the ideal state. Finally, the proposed estimation methods are verified by the simulation results.     

Modeling spatial density in low earth orbits using wavelets and random search

The events that occurred after 2007 such as Chinese anti-satellite test, explosion of Briz-M upper stage, break up of cosmos-2421 and collision of cosmos-2251 with Iridium-33 satellites have completely changed the spatial density patterns in low earth orbits. This has increased the risk of collision between active satellites and debris created by them. Aftermath, the risk assessment of possible collisions called as conjunction analysis of working satellites from day to day has become more crucial. Spatial density models are useful in understanding the long-term likelihood of a collision in a particular region of space and helpful in pre-launch orbit planning. In this paper we present an algorithmic procedure for automatically estimating exact model parameters corresponding to the peak location and number of peaks using wavelets that will speed up the parameter estimation process for the models with peaks.     

Costas编码信号的循环谱特征分析与参数估计

研究了准确反映Costas编码信号特点的关键参数集合。基于循环谱相关密度估计方法,分析了Costas编码的循环谱特征及其与辐射源侍号参数的对应关系,提出了一种没有先验知识条件下Costas编码截获信号特征提取方法。通过对典型的Costas编码信号参数提取归一化均方根误差的计算,验证了该算法的有效性。     

微小型无人机三轴磁强计现场误差校正方法

详细分析微小型无人机导航用三轴磁强计的误差来源,建立三轴磁强计的等效误差模型,提出基于两步估计算法和圆约束非对准误差估计算法的三轴磁强计现场误差校正方法.在充分考虑地磁场偏转和倾斜特性的基础上,提出适合微小型无人机应用的现场数据采样策略,能够在较少的旋转操作下获得较好的采样数据.仿真表明:在所有磁场误差都存在的情况下,...     

涡轴发动机自适应混合诊断模型高斯加权聚类方法

针对整个飞行包线内涡轴发动机健康参数估计问题,提出基于高斯加权聚类的机载自适应混合模型建立方法.机载自适应混合模型由卡尔曼滤波器和神经网络组成,由于飞行数据样本庞大,采用高斯加权模型对涡轴发动机飞行数据进行实时聚类,利用聚类数据更新神经网络权重,并实现自适应混合模型工作范围的自动扩展.仿真结果表明了该方法的有效性,采用...     

Regularized Robust Filter for Spacecraft Attitude Determination

A modified regularized robust filter is proposed for spacecraft attitude determination in the presence of relative misalignment of attitude sensors. The filter is designed to minimize the worst-possible residual norm on condition that there is parametric uncertainty in the measurement model. The weighting matrix of the residual norm is designed to minimize the upper bound of the estimation error variance. The performance of the proposed attitude determination robust filter is illustrated with the use of real test data from a real three-floated gyroscope. Simulation results demonstrate that the attitude estimation accuracy is improved by using the proposed algorithm.     

无人机空中加油过程中基于机器视觉的相对位姿估计(英文)

本文主要研究了在无人机空中自主加油过程中,利用安装在无人机上的摄像机得到的视觉图像确定无人机与加油机之间相对位置关系的算法。本文以经典的OI算法为基础展开研究,文章主要分为三个部分:首先简要介绍了OI算法的基本实现方法,指出了在OI算法中,旋转矩阵R的初值的选择对于位姿估计的精度有一定的影响。其次主要介绍了一种线性算法用于获取R初值的方法,同时针对空中加油过程中无人机与加油机之间的相对姿态角变化较小的特点,提出了可以将旋转矩阵R的初值设为单位矩阵。论文最后在仿真环境下对相关算法展开实验研究,同时研究了在高斯噪声环境下特征点在加油机表面的分布对位姿估计结果的影响,论文得到如下的结论:1、针对空中加油过程中无人机与加油机之间的相对姿态角变化较小的特点,在高斯噪声环境下,以单位矩阵作为旋转矩阵的初值得到的位姿估计的结果好于以线性算法为初值的结果。2、在相同的高斯噪声环境下,特征点之间的距离越大时得到的位姿估计结果的精度越高。3、特征点的数量对相对位姿估计的精度没有明显影响。