Combination of spatial networks using an estimated covariance matrix

When combining satellite and terrestrial networks, covariance matrices are used which have been estimated from previous data. It can be shown that the least-squares estimator of the unknown parameters using such an estimated covariance matrix is not necessarily the best. There are a number of cases where a more efficient estimator can be obtained in a different way. The problem occurs frequently in geodesy, since in least-squares adjustment of correlated observations estimated covariance matrices are often used. If the general structure of the covariance matrix is known, results can often be improved by a method called covariance adjustment. The statistical model used in least-squares collocation leads to a type of covariance matrix which fits into this framework. It is shown in which way improvements can be made using a modified approach of principal component analysis. As a numerical example the combination of a satellite and a terrestrial network has been computed with varying assumptions on the covariance matrix. It is shown which types of matrices are critical and where the usual least-squares approach can be applied without hesitation. Finally, a simplified representation of covariances for spatial networks by means of a suitable covariance function is suggested. Paper presented at the International Symposium on Computational Methods in Geometrical Geodesy-Oxford, 2–8 September, 1973.     

Approximating covariance matrices estimated in multivariate models by estimated auto- and cross-covariances

Quantities like tropospheric zenith delays or station coordinates are repeatedly measured at permanent VLBI or GPS stations so that time series for the quantities at each station are obtained. The covariances of these quantities can be estimated in a multivariate linear model. The covariances are needed for computing uncertainties of results derived from these quantities. The covariance matrix for many permanent stations becomes large, the need for simplifying it may therefore arise under the condition that the uncertainties of derived results still agree. This is accomplished by assuming that the different time series of a quantity like the station height for each permanent station can be combined to obtain one time series. The covariance matrix then follows from the estimates of the auto- and cross-covariance functions of the combined time series. A further approximation is found, if compactly supported covariance functions are fitted to an estimated autocovariance function in order to obtain a covariance matrix which is representative of different kinds of measurements. The simplification of a covariance matrix estimated in a multivariate model is investigated here for the coordinates of points of a grid measured repeatedly by a laserscanner. The approximations are checked by determining the uncertainty of the sum of distances to the points of the grid. To obtain a realistic value for this uncertainty, the covariances of the measured coordinates have to be considered. Three different setups of measurements are analyzed and a covariance matrix is found which is representative for all three setups. Covariance matrices for the measurements of laserscanners can therefore be determined in advance without estimating them for each application.     

基于随机模型基准的Helmert方差分量估计

在多类观测数据联合平差中,存在某类观测值的验前协方差阵正确而其他类不正确的情况,传统Helmert方差分量估计在求解此类问题时没有对正确的验前协方差阵加以区别,从而造成正确的随机模型经估计后同样也被调整。针对上述情况,首先分析了Helmert方差分量估计迭代收敛结果的实质,然后提出了随机模型基准的概念,并推导了基于随机模型基准的Helmert方差分量估计公式。经计算表明,新公式完全可行,可以用于解决实际问题。     

方差分量估计理论及其在整体大地测量中的应用

本文探讨了整体大地测量平差中的方差分量估计问题,给出了自适应最小二乘配置;不仅较好地解决了各类观测量权比的确定问题,而且能够确定信号的方差因子。此外,文章还对如何更好地确定局部地区扰动位及其泛函的协方差函数作了讨论;最后通过对一个实测网进行了整体平差,并与经典平差结果进行了比较。     

Datum definition by free net adjustment

An adjustment method of angular and distance measurements for densification of horizontal control is presented, where the resulting network has the properties of a free net. The higher order control points form an integral part of the net and serve as a source for datum definition. The relative positions of the points in the net are independent of the higher order control points and are determined by the measurements and their respective covariance matrix. The solution is equivalent to the orthogonal transformation of a minimum constraints solution of a net where the transformation parameters are determined by the control points and their a—priori covariance matrix.     

顾及框架点坐标误差的三维基准转换严密模型

框架点坐标是由观测数据通过平差得到的,不可避免地受到观测误差的影响。针对原框架和目标框架坐标均存在误差、非公共点与公共点间存在相关性,以及转换系数矩阵中仅部分元素存在误差的实际情况,提出了同时考虑框架内误差以及转换点间相关性的基准转换严密模型,该模型将公共点和非公共点联合处理,同时计算坐标转换参数和所有点的坐标转换值,推导出了新的严格坐标转换公式,该公式为传统坐标转换公式基础上增加一改正量的形式;进一步,推导了原框架和目标框架坐标的方差不一致情况下的坐标转换模型的自适应解法;最后,利用"陆态网络工程"2000个区域站的实测坐标进行坐标转换验证,结果表明,这种严密模型较传统坐标转换模型具有更高的坐标转换精度。     

Lq估计的渐近方差-协方差矩阵及其特点

针对由独立同分布误差膨胀而成的独立不等精度误差,根据未知参数的M估计的Bahadur型线性表达式,本文导出了由观测量、残差向量、参数估计量和观测量平差向量组成的基本向量的Bahadur型表达式.进一步地,根据方差传播定律导出了M估计的基本向量的渐近方差-协方差矩阵,该矩阵由3个多余参数决定,第三多余参数由本文定义.对Lq范估计,分别计算了误差分别为正态分布和q范分布时的3个多余参数,以及相应的基本向量的方差协方差矩阵.对最小二乘估计,残差向量与参数估计量和观测量的平差向量统计独立,相应的协方差矩阵为零,这一性质与误差分布无关.对正态分布的Lq估计,残差向量与参数估计量和观测量平差向量的协方差不为零;而对q范分布的Lq估计,即是相应的极大似然估计,残差向量与参数估计量和观测量平差向量的协方差为零.文中所得公式和结论可用于统计分析.     

推广的半动态法及其在多级工程控制网精度评定中的应用

为了合理地求定按附合网平差的网点坐标的实际精度,本文推广了半动态法,籍此可在首级网的基准下,统一求定顾及各级控制网测量误差影响精度的全部网点坐标的方差—协方差阵。本文所提出的方法适用于种种情况下的精度估计:等级数不受限制的平面、高程或三维网,本级网平差中的固定数据以及待估计的平差值函数可以包含不同级的网点,从而克服了半动态法原有的局限性。     

OUTLIER DETECTION AND RELIABILITY OF ADJUSTMENT MODELS WITH SINGULAR COVARIANCE MATRICES

Up to now,outlier detection and reliability theory are generallybased on the regular Gauss-Markov models,in which the covariance matrix of ob-servations is positively definite.For the adjustment models with singular covari-ance matrix,the statistics for outlier detection are derived by the authors.Thecorresponding reliability theory is developed.And the application of the theory isdemonstrated with a practical example.     

On the sensitivity of the results of least-squares adjustments concerning the stochastic model

A proper perturbation theory of a mathematical model and the quantities derived by means of least-squares adjustments is indispensable if the results have to be interpreted in a wider context. The sensitivity of some characteristic results of least-squares adjustments such as the estimated values of the parameters and their variance–covariance matrix due to imminent uncertainties of the stochastic model is discussed in detail. Linearizations are used with rigorous error measures and interval mathematics. Numerical examples conclude the investigations. Received: 27 December 1997 / Accepted: 19 April 1999     

Estimability analysis of variance and covariance components

Although variance and covariance components have been extensively investigated and a number of elegant formulae to compute them have been derived, nothing is known, without any ambiguity, about their estimability in the case of a fully unknown variance–covariance matrix. We prove that variance and covariance components in this case are not estimable, thus clarifying the ambiguity of the literature on the topic and correcting some erroneous statements in the literature. We also give a new theorem on the estimability of a linear function of variance and covariance components. Then we propose a new method to estimate the variance–covariance matrix with special structure, which can presumably be represented by, at most, r(r + 1)/2 independent parameters to guarantee its estimability in such a subspace, by directly implementing the positive definiteness of the matrix as constraint to the restricted maximum likelihood method, where r is the number of redundant measurements. Therefore, our estimates of the variance and covariance components always reconstruct a positive definite matrix and are always physically meaningful.     

用Givens变换解最小二乘问题

Ｇｉｖｅｎｓ变换提供一个不通过组成法方程解最小二乘问题的途径。这种解法具有数值稳定性好、计算效率高和节省内存等优点，它也适用于序贯平差。文中首先介绍了Ｇｉｖｅｎｓ变换的概念，叙述了用Ｇｉｖｅｎｓ变换解最小二乘问题的计算步骤，最后提供了更新未知参数及其协方差矩阵的算法。作为附录，给出了法方程矩阵的三角分解，以补充说明设计矩阵的Ｇｉｖｅｎｓ变换结果。     

相关平差的导线点精度

目前导线网平差多采用相关平差法。按该法在网(结点)平差时计算未知数的协因数阵,可以评定结点的点位精度。文献[2]等均未曾论及各个导线点的精度评定问题。实际上,导线网中的最弱点不一定出现在结点内,为此需全面评定导线点的精度。本文将专门论述导线网相关平差中导线点的精度评定问题。     

奇异平差模型的粗差检验与可靠性理论

本文推导了奇异平差模型参数估计的一般公式，构造了粗差检验的统计量，通过假设检验研究了相应的可靠性理论，并借助于一个实例阐述了这一理论的应用。     

导线网坐标差观测值相关平差方法研究

提出了利用普通间接平差和相关间接平差两种平差方法相结合,进行以坐标差作为观测值进行导线网相关平差的方法.在该平差方法中,利用普通的简介平差原理推求出坐标差观测值的相关协因数阵,并以此作为已知数据进行以坐标差观测值作为观测值的导线网的相关平差.     

天文大地网与GPS2000网联合平差数据处理方法

介绍了天文大地网与GPS2000网联合平差数据处理中的主要数学模型、大规模稀疏矩阵的有效解算方法，用Helmert方差分量估计算法进行地面网的方差分量估计，并用该方法对我国天文大地网的观测数据及空间网数据进行联合平差，得出全网平均点位的点位中误差为o．11m，点位精度95．5％优于0．3m。     

Approximate solution of normal equations by eigenvalue decomposition

Geodetic adjustment problems frequently require the solution of large systems of linear equations. An approximation method is presented based on the decomposition of the estimated covariance matrix of the observation matrix, calculated in a pre-processing step, into a system of eigenvalues and eigenvectors. Neglecting the non-dominant eigenvalues and the assigned eigenvectors, the matrix of the residuals is approximated applying the synthesis formula of principal-component analysis. Although the number of observation vectors in the multivariate Gauss–Markoff model is drastically reduced, all unknown parameters are estimated approximately. The described method is tested using a numerical example of satellite altimetry. Received: 6 January 1997 / Accepted: 16 November 1998     

Effects of errors-in-variables on weighted least squares estimation

Although total least squares (TLS) is more rigorous than the weighted least squares (LS) method to estimate the parameters in an errors-in-variables (EIV) model, it is computationally much more complicated than the weighted LS method. For some EIV problems, the TLS and weighted LS methods have been shown to produce practically negligible differences in the estimated parameters. To understand under what conditions we can safely use the usual weighted LS method, we systematically investigate the effects of the random errors of the design matrix on weighted LS adjustment. We derive the effects of EIV on the estimated quantities of geodetic interest, in particular, the model parameters, the variance–covariance matrix of the estimated parameters and the variance of unit weight. By simplifying our bias formulae, we can readily show that the corresponding statistical results obtained by Hodges and Moore (Appl Stat 21:185–195, 1972) and Davies and Hutton (Biometrika 62:383–391, 1975) are actually the special cases of our study. The theoretical analysis of bias has shown that the effect of random matrix on adjustment depends on the design matrix itself, the variance–covariance matrix of its elements and the model parameters. Using the derived formulae of bias, we can remove the effect of the random matrix from the weighted LS estimate and accordingly obtain the bias-corrected weighted LS estimate for the EIV model. We derive the bias of the weighted LS estimate of the variance of unit weight. The random errors of the design matrix can significantly affect the weighted LS estimate of the variance of unit weight. The theoretical analysis successfully explains all the anomalously large estimates of the variance of unit weight reported in the geodetic literature. We propose bias-corrected estimates for the variance of unit weight. Finally, we analyze two examples of coordinate transformation and climate change, which have shown that the bias-corrected weighted LS method can perform numerically as well as the weighted TLS method.     

平面控制网相对点位误差的合理评定与分析

时平面控制网的相对点位精度评定问题做了一些理论分析,探讨了在不同基准条件下协因数阵的转换关系,及依据转换后的协因数阵来评定相对点位精度的问题.在有关数学模型推导的基础上,利用MATLAB语言编制了控制网平差及相对点位精度的评定程序,并结合具体的工程进行了算例分析.     

Pre-zero-epoch covariance matrix in sequential analysis of deformations

A sequential adjustment procedure is proposed for the direct estimation of point—velocities in deformation analysis networks. At any intermediate stage of the adjustment the up-to-date covariance matrix of those velocities tells the evolving story of the network in terms of solvability and reliability. A pre-zero-epoch covariance matrix is utilized for a smooth and flexible treatment of two characteristic problems of deformation analysis:

- high turnover of points in the network

- processing variable and generally incomplete observational batches.

A small numerical example is presented at the end as an illustration.