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。     

GNSS receiver clock modeling when using high-precision oscillators and its impact on PPP

Processing data from Global Navigation Satellite Systems (GNSS) always requires time synchronization between transmitter and receiver clocks. Due to the limited stability of the receiver’s internal oscillator, the offset of the receiver clock with respect to the system time has to be estimated for every observation epoch or eliminated by processing differences between simultaneous observations. If, in contrast, the internal oscillator of the receiver is replaced by a stable atomic clock one can try to model the receiver clock offset, instead of estimating it on an epoch-by-epoch basis. In view of the progress made in the field of high-precision frequency standards we will investigate the technical requirements for GNSS receiver clock modeling at the carrier phase level and analyze its impact on the precision of the position estimates.     

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。     

数传控制装备可靠性预计

分析了某型数传控制装置可靠性模型,采用应力分析法对该装置进行了可靠性预计,根据设备实际状况求得其各主要部件的失效率,评估了产品的可靠性,从数据分析得出了影响可靠性的主要环节,提出了改进措施。通过可靠性试验,对可靠性预计值进行了验证。     

基于HOSVD局部重组的利噪抑噪经验模式分解及应用

及时准确地识别航天机构萌生和发展的损伤故障特征信息,可为机构故障诊断评估、科学任务调整以及未来在轨维修提供科学决策依据。集成噪声重构经验模式分解(ENEMD)及其衍生方法都是基于噪声利用机制以原信号中估计噪声改善模式混淆并实现信号降噪。然而,该方法中奇异值拐点难以获取、阈值处理中噪声不连续等带来的噪声估计偏差,将降低微弱特征提取准确性。为此,提出一种基于高阶奇异值分解(HOSVD)局部重组的噪声估计技术。研究基于滑动窗截断和Hankel矩阵相结合的张量构建,然后将奇异值曲率谱上的最大峰值点作为合理奇异阶,最后根据选取的奇异阶重构张量分解模型得到所需的估计噪声分量。在此基础上,将HOSVD局部重组引入ENEMD方法中,提出利噪抑噪经验模式分解方法。该方法可进一步提高微弱噪声估计精确度,实现对航天机构损伤微弱特征的增强提取。仿真分析和某航天轴承试验案例验证了该方法在损伤微弱特征提取和识别上具有实用性与有效性。