自由网平差方法及统计特性新探

本文结合参数平差和主成分估计理论,导出了误差方程中含多重共线性时未知参数的求解公式,并以定量的形式,证明了主成分估计的解是极小范数解。     

用主成分法求解失亏自由网的研究

结合参数平差和主成分估计理论,导出误差方程中含多重共线性关系时求解未知数的公式,并证明主成分估计的解是最小二乘最小范数解。由此,将主成分估计推广到秩亏自由网平差中,导出求解未知参数及其协因数的公式,并据主成分估计演绎出用附有条件的参数工法求解秩亏自由网的条件方程。     

用主成分法求解秩亏自由网的研究

结合多数平差和主成分估计理论,导出误差方程中含多重共线性关系时求解未知数的公式,并证明主成分估计的解是最小二乘最小范效解。由此,将主成分估计推广到秩亏自由网平差中,导出求解未知多数及其协因数的公式,并据主成分估计演绎出用附有条件的参数平差法求解秩亏自由网的条件方程（基准在达式）。     

主成分估计与附加条件的参数平差

证明了主成分估计实质上是附着条件的参数平差，并根据此性质结合算例分析了主成分估计的局限性，提出了改进方法。     

岭型组合主成分估计及误差影响

将岭型组合主成分估计用于测量平差,定义了平差参数的岭型组合主成分估计,推导了它的验后精度─均方误差及其误差影响函数,讨论了它的优良性质,并且证明了在一定条件下,它具有比最小二乘估计和岭估计更好的抗干扰性和抗差性。最后,用实例验证了其理论的正确性。     

抗差岭型组合主成分估计及误差影响

本文从有偏估计类中的岭型组合主成分估计出发 ,结合抗差估计理论 ,利用抗差M估计模型 ,提出了一种新的抗差有偏估计法———抗差岭型组合主成分估计。推导了平差参数的抗差岭型组合主成分估计解 ,以及平差参数的验后精度和误差影响函数。计算结果表明 ,抗差岭型组合主成分估计不但能克服法方程系数阵病态性的影响 ,而且能有效地抵制观测值中精差的异常干扰 ,使参数的解更为准确可靠     

极大验后估计及其在扩建网中的应用

本文基于未知参数具有先验正态分布的广义Ｇ－Ｍ模型，推导了未知参数和方差因子的极大验后估计公式，证明了未知和的极大验后估计是无偏、有效估计量，方差因子的极大验后估计有偏，并推导了方差因子的边缘极大验后估计，证明了它的无偏及有效性，作为应用，本文最后证明了扩建网极大验后平差成果等于新旧网整体平差成果。     

附有条件的参数平差模型的有偏估计

讨论附有条件的参数平差模型参数的有偏估计问题。提出了约束岭估计和约束主成分估计,并证明了它们的优良性质,最后给出了一个算例,验证了所得结果。     

半参数平差模型参数的两步估计

考虑半参数平差模型L=Bx+S+Δ,xεRd,S为未知回归参数,为未知Borel函数。本文首先利用自然样条函数法,找到符合条件的非参数自然样条插值函数。其次利用偏残差法并综合最小二乘法,导出了参数和非参数的解算公式,讨论了窗宽参数的选取方法。在本文的最后,将这种估计方法应用到重力场的计算中,说明了利用半参数平差模型估计参数的有效性。     

在无偏估计类中改进最小二乘估计的方法

当平差模型中存在复共线关系时，未知参数的最小二乘估计很不可靠。提出了在无偏估计类中解决这一问题的有效方法—附加条件法，并从理论上证明了这一方法。     

Extended free net adjustment constraints

The contribution of geodetic measurements to the establishment of a control network can be partitioned into global and local (individual) components. The global component epitomized in a number of geometrically meaningful parameters can be estimated together with the individual point coordinates. The additional rank defect created by the extension of the parameter list is corrected by free net adjustment constraints which are extended beyond those needed for a solution of the network datum problem. Two applications of extended free net adjustment are outlined and illustrated by elementary numerical examples. A non-Cartesian (skew) reference system discussed in the Appendix provides an exotic interpretation of the estimated global and individual parameters. Prepared during a grant period (September 1984 through February 1985) while serving as a Visiting Senior Scientist in Geodesy, National Research Council, National Academy of Sciences, Washington, D.C.     

导线网的精度分析

本文按条件平差原理通过机助法对5类34种典型图形进行模拟估算,经过分析比较,得出布设导线网时的一些结论及各类导线网最弱点的精度估算公式,供规范修改和设计时参考。     

An iterative solution of weighted total least-squares adjustment

Total least-squares (TLS) adjustment is used to estimate the parameters in the errors-in-variables (EIV) model. However, its exact solution is rather complicated, and the accuracies of estimated parameters are too difficult to analytically compute. Since the EIV model is essentially a non-linear model, it can be solved according to the theory of non-linear least-squares adjustment. In this contribution, we will propose an iterative method of weighted TLS (WTLS) adjustment to solve EIV model based on Newton–Gauss approach of non-linear weighted least-squares (WLS) adjustment. Then the WLS solution to linearly approximated EIV model is derived and its discrepancy is investigated by comparing with WTLS solution. In addition, a numerical method is developed to compute the unbiased variance component estimate and the covariance matrix of the WTLS estimates. Finally, the real and simulation experiments are implemented to demonstrate the performance and efficiency of the presented iterative method and its linearly approximated version as well as the numerical method. The results show that the proposed iterative method can obtain such good solution as WTLS solution of Schaffrin and Wieser (J Geod 82:415–421, 2008) and the presented numerical method can be reasonably applied to evaluate the accuracy of WTLS solution.     

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.     

自适应序贯平差及其应用

基于自适应估计原理和抗差M估计原理，提出了自适应序贯平差和自适应抗差序贯平差法，给出了相应的参数估值公式。计算结果证明，自适应序贯平差能有效地控制模型参数先验信息的异常影响，平衡观测值和参数先验值在参数估计中的贡献;自适应抗差序贯平差能有效地抵制观测异常和模型参数先验信息异常扰动对平差结果的影响。     

Least squares adjustment and collocation

Summary For the estimation of parameters in linear models best linear unbiased estimates are derived in case the parameters are random variables. If their expected values are unknown, the well known formulas of least squares adjustment are obtained. If the expected values of the parameters are known, least squares collocation, prediction and filtering are derived. Hence in case of the determination of parameters, a least squares adjustment must precede a collocation because otherwise the collocation gives biased estimates. Since the collocation can be shown to be equivalent to a special case of the least squares adjustment, the variance of unit weight can be estimated for the collocation also. This estimate gives the scale factor for the covariance matrices being used in the collocation. In addition, the methods of testing hypotheses and establishing confidence intervals for the parameters of the least squares adjustment may be applied to the collocation.     

有偏估计、参数加权平差及秩亏自由网平差的关系与比较

在阐述了有偏估计、参数加权平差和附加约束法秩亏平差的概念及性质等基本理论的基础上, 着重分析了这几种平差方法之间的关系和异同点。