利用条件平差实现导航星座自主守时方法

针对导航星座自主导航系统的重要组成部分———自主守时系统,提出了一种简便易行的新算法:根据星间测距数据,采用条件平差进行自主守时计算;并且介绍了此方法基本原理和实现思路。自主守时仿真计算表明,此方法守时同步误差不累积,距离测量误差0.1m时,时间同步误差均方根小于0.4m。     

基于杆臂补偿的多MIMU六方位倍速率标定方法

误差建模、标定与补偿是提高微惯性测量单元(MIMU)精度的关键,而杆臂效应是影响低成本微机电系统(MEMS)惯导精度进一步提升的瓶颈.针对杆臂效应引起的多MIMU标定误差问题,提出了一种基于杆臂补偿的多MIMU六方位倍速率标定方法.分析了杆臂效应影响多MIMU标定的误差机理,设计了一种六方位倍速率标定方案,建立了基于杆...     

递推的TLS参数估计方法及其在飞轮FDD中的应用

飞轮是很多卫星姿态控制系统中的重要执行部件，其星上自主故障诊断是一个很有意义的研究课题。本文根据飞轮系统的特点，在最小二乘（ＬＳ）参数估计方法的基础上提出了全最小二乘（ＴＬＳ）参数估计的递推形式，证明了它对飞轮参数估计的收敛性，并用于飞轮缓变故障的检测和诊断，取得了较好结果。     

多传感器目标识别的优化融合

对于工作在复杂环境下的多传感器目标识别系统,确保其稳健性和准确性的关键是有效处理被融合信息的不确定性。根据影响信息不确定性的因素,文章把传感器本地识别信息的可信度分为了统计可信度和环境可信度;采用最小二乘法和神经网络实现统计可信度的估计,自适应神经模糊推理实现环境可信度估计;并利用这两种可信度实现以一致理论为基础的多传感器目标识别的优化融合。经实验仿真证明,该融合方案是有效的。     

预报误差法在伺服系统时域辨识中的应用与实现

介绍了系统辨识中的预报误差法的基本原理,设计了一种简洁通用的软硬件辨识平台。成功地应用预报误差的辨识方法对一个实际的中功率雷达伺服系统实现了时域辨识,得到了该伺服系统比较准确的模型。将辨识结果与一般最小二乘法的辨识结果进行了比较,发现此辨识方法在抑制噪声方面有很大的改进。     

用最小二乘法评定圆柱度误差的理论与算法

建立了用最小二乘法评定圆柱度误差的理论与算法,并对圆柱度误差进行了定量分析和定性分析,给出了误差分离的定量计算公式,将其分离成形状误差、参数误差和方向误差,并指出了每种误差的补偿原则。所推导的数学模型简单、明了,具有推广价值。     

Convex optimisation approach to constrained fuel optimal control of spacecraft in close relative motion

This paper describes an interesting and powerful approach to the constrained fuel-optimal control of spacecraft in close relative motion. The proposed approach is well suited for problems under linear dynamic equations, therefore perfectly fitting to the case of spacecraft flying in close relative motion. If the solution of the optimisation is approximated as a polynomial with respect to the time variable, then the problem can be approached with a technique developed in the control engineering community, known as “Sum Of Squares” (SOS), and the constraints can be reduced to bounds on the polynomials. Such a technique allows rewriting polynomial bounding problems in the form of convex optimisation problems, at the cost of a certain amount of conservatism. The principles of the techniques are explained and some application related to spacecraft flying in close relative motion are shown.     

无网格方法中含参数指数权函数的研究

选择合适的权函数及相应的影响半径是无网格方法中很重要的问题,在引入数值紧支域概念的基础上分析了含参数的指数权函数中参数的选取范围,综合考虑计算效率和精度后给出了参数的选取原则,并讨论了在特定的参数下的最优半径以及不同参数对最优半径的影响.     

Accuracy analysis of GNSS-IR snow depth inversion algorithms

In recent years, with the continuous development of Global Navigation Satellite System (GNSS), it has been applied not only to navigation and positioning, but also to Earth surface environment monitoring. At present, when performing GNSS-IR (GNSS Interferometric Reflectometry) snow depth inversion, Lomb-Scargle Periodogram (LSP) spectrum analysis is mainly used to calculate the vertical height from the antenna phase center to the reflection surface. However, it has the problem of low identification of power spectrum analysis, which may lead to frequency leakage. Therefore, Fast Fourier Transform (FFT) spectrum analysis and Nonlinear Least Square Fitting (NLSF) are introduced to calculate the vertical height in this paper. The GNSS-IR snow depth inversion experiment is carried out by using the observation data of P351 station in PBO (Plate Boundary Observatory) network of the United States from 2013 to 2016. Three algorithms are used to invert the snow depth and compared with the actual snow depth provided by the station 490 in the SNOTEL network. The observations data of L1 and L2 bands are respectively used to find the optimal combination between different algorithms further to improve the accuracy of GNSS-IR snow depth inversion. For L1 band, different snow depths correspond to different optimal algorithms. When the snow depth is less than 0.8 m, the inversion accuracy of NLSF algorithm is the highest. When the snow depth is greater than 0.8 m, the inversion accuracy of FFT algorithm is higher. Therefore, according to the different snow depth, a combined algorithm of NLSF + FFT is proposed for GNSS-IR snow depth inversion. Compared with the traditional LSP algorithm, the inversion accuracy of the combined algorithm is improved by 10%. For L2 band data, the results show that the accuracy of snow depth inversion of various algorithms do not change with the variations of snow depth. Among the three single algorithms, the inversion accuracy of FFT algorithm is better than that of LSP and NLSF algorithms.     

加速度计温控系统被控对象建模技术研究

加速度计工作过程中,其内部与外界环境存在温度梯度,针对温度变化影响加速度计参数的问题,提出了一种加速度计温控系统被控对象建模的方法。进行了多工作点加速度计升温和降温试验,采用递推增广最小二乘法对被控对象模型进行了辨识,建立了加速度计温控系统被控对象模型和降温模型,计算结果表明,3min后温控系统被控对象模型曲线残差在-0.5~0.5℃之间,降温模型曲线残差在-0.15~0.15℃之间,满足系统对加速度计性能需求,表明了方法的有效性。