通用EIV平差模型及其加权整体最小二乘估计

以平差基本理论为基础,提出了EIV(errors-in-variables)平差模型的通用形式,涵盖了间接平差、条件平差、附有参数的条件平差及附有限制条件的间接平差等基本EIV模型形式。基于整体最小二乘估计准则,研究了通用EIV模型的加权整体最小二乘算法,并推导了估计结果的近似精度公式。通用EIV模型及其整体最小二乘算法是对EIV模型估计理论的进一步完善,统一的整体最小二乘算法有利于软件的编程实现,有助于推动EIV模型估计理论的应用。     

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.     

加权整体最小二乘EIV模型与算法——与"加权整体最小二乘EIO模型与算法"一文的讨论

加权整体最小二乘方法是一种能同时顾及EIV（errors-in-variables）模型中系数矩阵和观测向量误差的参数估计方法。根据不同的应用场景,EIV模型则表现出不同的结构特征。"加权整体最小二乘EIO模型与算法"一文采用EIO模型处理EIV模型中的结构化问题^*。为了将其与现有方法进行对比,本文罗列出4种处理EIV模型结构特征的方法,并归纳了8种参数估计公式。同时从精度评定的角度讨论了整体最小二乘解的一阶及更高阶精度近似评定方法。需要强调的是,针对EIV模型及其参数估计理论可以从函数模型、随机模型和参数估计方法3个方面展开研究,但各方法殊途同归。     

二维直角建筑物规则化的加权总体最小二乘平差方法

针对基于遥感数据的二维建筑物的直角化问题,以建筑物边界点的坐标为观测值,以顾及边界正交限制条件的直线斜率和截距为参数,建立附有限制条件的变量误差（errors-in-variables,EIV）模型。考虑观测向量和设计矩阵相关的情况,给出了增广设计矩阵的协方差阵的计算方法,推导了附限制条件的通用加权总体最小二乘（weighted total least squares,WTLS）平差算法,以及近似精度评定算法和仅含二次型限制条件的WTLS平差方法。理论和算例分析表明,在建筑物重建问题中,附有限制条件的EIV模型比经典附有限制条件的Gauss-Helmert模型易于构建,所提的WTLS算法快速收敛速度快,对拓展WTLS平差方法的应用具有理论与实践意义。     

坐标转换Partial-EIV总体最小二乘方法

在测量数据处理过程中,针对系数矩阵中同时存在随机元素和固定元素的情况,Xu等通过将随机元素分离使EIV模型推广到Partial-EIV模型,并给出基于Partial-EIV模型的总体最小二乘(TLS)算法。文中介绍该算法,并将其应用在平面及空间的坐标转换中,通过与最小二乘(LS)、总体最小二乘(TLS)及加权总体最小二乘(WTLS)方法的比较和分析,验证该算法有效性。     

基于EIV模型的稳健估计

EIV（error-in-variables）模型同时考虑观测向量和系数矩阵的误差,自提出以来便得到广泛应用。目前针对EIV模型的整体最小二乘解法（TLS）假设观测值仅含有偶然误差,当观测值存在粗差时其解并不是最优的。文中通过选定合适的权函数,结合加权整体最小二乘迭代算法,导出基于EIV模型的稳健整体最小二乘迭代解法（RTLS）。线性拟合实验表明,文中方法能对粗差进行定位,且估计量受粗差影响较小,具有稳健性。     

On non-combinatorial weighted total least squares with inequality constraints

Observation systems known as errors-in-variables (EIV) models with model parameters estimated by total least squares (TLS) have been discussed for more than a century, though the terms EIV and TLS were coined much more recently. So far, it has only been shown that the inequality-constrained TLS (ICTLS) solution can be obtained by the combinatorial methods, assuming that the weight matrices of observations involved in the data vector and the data matrix are identity matrices. Although the previous works test all combinations of active sets or solution schemes in a clear way, some aspects have received little or no attention such as admissible weights, solution characteristics and numerical efficiency. Therefore, the aim of this study was to adjust the EIV model, subject to linear inequality constraints. In particular, (1) This work deals with a symmetrical positive-definite cofactor matrix that could otherwise be quite arbitrary. It also considers cross-correlations between cofactor matrices for the random coefficient matrix and the random observation vector. (2) From a theoretical perspective, we present first-order Karush–Kuhn–Tucker (KKT) necessary conditions and the second-order sufficient conditions of the inequality-constrained weighted TLS (ICWTLS) solution by analytical formulation. (3) From a numerical perspective, an active set method without combinatorial tests as well as a method based on sequential quadratic programming (SQP) is established. By way of applications, computational costs of the proposed algorithms are shown to be significantly lower than the currently existing ICTLS methods. It is also shown that the proposed methods can treat the ICWTLS problem in the case of more general weight matrices. Finally, we study the ICWTLS solution in terms of non-convex weighted TLS contours from a geometrical perspective.     

部分变量误差模型的整体抗差最小二乘估计

部分变量误差模型(partial EIV model)的加权整体最小二乘(weighted total least-squares,WTLS)估计不具备抵御粗差的能力。鉴于粗差可能同时出现在观测值和系数矩阵中,本文在提出部分变量误差模型WTLS估计的两步迭代解法的基础上,运用抗差M估计的等价权方法,发展了一种整体抗差最小二乘(TRLS)估计方法,并采用一致最大功效统计量确定降权因子。针对WTLS估计两步迭代解法的特点,设计了两个不同的降权方案:第1个方案是在估计系数矩阵元素时,不对观测值降权,仅对系数矩阵降权;第2个方案是在估计系数矩阵元素时,既对系数矩阵降权,同时也对观测值降权。通过对模拟2D仿射变换和线性拟合实例进行计算和分析,结果表明第1方案优于第2方案,并且优于基于残差和验后单位权方差的抗差估计和现有的变量误差模型抗差估计。     

基于改进目标函数的partial EIV模型WTLS估计的新算法

针对部分变量误差（partial EIV）模型的加权整体最小二乘（weighted total least squares,WTLS）估值的计算需要多次迭代且效率低下的情况,根据加权LS（least square）原理,通过改进目标函数,并运用矩阵微分运算以及矩阵反演变换,提出了一种计算partial EIV模型WTLS估值的新算法。算例计算结果表明,新算法具有迭代次数少、计算效率高等优点。     

基于非线性高斯-赫尔默特模型的混合整体最小二乘估计

针对EIV模型的系数矩阵同时包含固定量和随机量的情况,通过将系数矩阵中的随机量提取出来纳入平差的随机模型,从而将EIV模型表示为非线性高斯-赫尔默特(Gauss-Herlmert,GH)模型形式,推导了混合LS-TLS(least squares-total least squares,LS-TLS)算法及其精度估计公式。算法适用于系数矩阵包含固定列、固定元素和随机元素的一般情况。模拟实例结果表明,混合LS-TLS算法与已有能够解决系数矩阵同时含固定量和随机量的结构性或加权TLS算法的估计结果一致;混合LS-TLS的估计结果统计上要优于LS或TLS估计结果。     

Total least squares adjustment in partial errors-in-variables models: algorithm and statistical analysis

The weighted total least squares (TLS) method has been developed to deal with observation equations, which are functions of both unknown parameters of interest and other measured data contaminated with random errors. Such an observation model is well known as an errors-in-variables (EIV) model and almost always solved as a nonlinear equality-constrained adjustment problem. We reformulate it as a nonlinear adjustment model without constraints and further extend it to a partial EIV model, in which not all the elements of the design matrix are random. As a result, the total number of unknowns in the normal equations has been significantly reduced. We derive a set of formulae for algorithmic implementation to numerically estimate the unknown model parameters. Since little statistical results about the TLS estimator in the case of finite samples are available, we investigate the statistical consequences of nonlinearity on the nonlinear TLS estimate, including the first order approximation of accuracy, nonlinear confidence region and bias of the nonlinear TLS estimate, and use the bias-corrected residuals to estimate the variance of unit weight.     

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.     

基于变量投影的结构总体最小二乘算法

测绘领域诸多实际应用中系数矩阵和观测向量具有结构特征,即系数矩阵和观测向量中包含固定量(甚至固定列)和随机量,并且不同位置的随机量线性相关。针对这个问题,从变量误差(errors-in-variables,EIV)函数模型出发,首先,将系数矩阵和观测向量构成的增广矩阵表示为仿射函数形式,并采用变量投影法对函数模型进行重构;然后,利用拉格朗日法推导出了一种结构总体最小二乘(structured total least squares,STLS)估计算法。算例分析结果表明,该算法与已有能够解决系数矩阵和观测向量存在结构特征的加权或结构总体最小二乘算法估计结果一致,说明了该算法的有效性,同时阐明了该算法与已有相关算法的关系。     

加权和不加权TLS方法及其在不等精度坐标变换中的应用

以重合点坐标独立但不等精度的三维坐标变换问题为基础,采用不加权和加权的TLS方法进行解算。模拟算例表明,未加权的简单TLS方法与基于残差的LS方法的估计结果一致。在加权方法中,按行分块独立的WTLS方法能达到最大似然估计精度,而EWTLS方法由于未考虑元素间的相关性,估计精度略低。     

PEIV模型WTLS估计的Fisher-Score算法

考虑系数矩阵含非随机元素和不同位置含相同随机元素的结构化特征,PEIV（partial errors-in-variables）模型较一般的EIV模型更为严格。现有PEIV模型加权整体最小二乘（weighted total least squares,WTLS）估计算法需多次迭代,影响计算效率。通过利用观测值误差和系数矩阵误差的统计性质构造非线性目标函数,并以此推导了新的PEIV模型WTLS估计的计算公式,同时设计了相应的Fisher-Score算法。算例分析结果表明,相比较而言,Fisher-Score算法迭代次数较少,计算效率得到大大提升。     

一种相关观测的Partial EIV模型求解方法

Partial errors-in-variables(Partial-EIV)模型作为EIV模型的扩展形式,其构造方式更有规律,解算方法更为简便,能有效应用于实际情况。针对已有Partial EIV模型方法未考虑观测向量和系数矩阵存在相关性这一情况,通过提取观测向量和系数矩阵组成的增广矩阵中非重复出现的随机元素,构建更具一般适用性的Partial EIV模型,在该模型的基础上,将特殊假定条件扩展到不限定观测数据相关性的一般情况,详细推导了观测向量和系数矩阵元素相关且不等精度情况下的加权总体最小二乘方法,通过算例试验,并与目前已有的解决EIV模型相关观测情况下的方法进行了比较分析,研究表明本文方法可以提高计算效率,更具一般性,特别是对于观测向量和系数矩阵中存在常数元素和重复元素的情况。     

加权总体最小二乘的效率优化算法

加权总体最小二乘法是理论上估计EIV模型参数相对严密的方法,其迭代过程中涉及的矩阵运算较为耗时,在处理大量级数据时尤其明显。PEIV模型有助于提高加权总体最小二乘法的计算效率。本文基于PEIV模型和经典最小二乘准则给出了一种加权总体最小二乘法算法,算法的推导过程简洁,易于理解,迭代过程中无需重构矩阵,减少了矩阵运算量。最后通过仿真试验验证了算法的可靠性。试验结果表明,本文算法可以取得与现有算法相同的参数估计精度且计算效率更高。     

动态EIV模型及其总体卡尔曼滤波方法

针对求解动态EIV模型时未考虑状态方程中状态转移矩阵误差的问题,本文建立了一种能够同时顾及状态方程和观测方程中各量误差的动态EIV模型。推导了针对该动态EIV模型的总体卡尔曼滤波方法及其近似精度评定公式。对比分析了本文总体卡尔曼滤波方法与已有总体卡尔曼滤波方法及总体最小二乘方法的异同。算例结果表明,本文方法统计上要优于标准卡尔曼滤波方法和已有的总体卡尔曼滤波方法。     

An approach to response-based reliability analysis of quasi-linear Errors-in-Variables models

The paper presents an approach to internal reliability analysis of observation systems known as Errors-in-Variables (EIV) models with parameters estimated by the method of least squares. Such problems are routinely treated by total least squares adjustment, or orthogonal regression. To create a suitable environment for derivations in the analysis, a general nonlinear form of such EIV models is assumed, based on a traditional adjustment method of condition equations with unknowns, also known as the Gauss–Helmert model. However, in order to apply the method of reliability analysis based on the approach to response assessment in systems with correlated observations, presented in the earlier work of this author, it was necessary to confine the considerations to a quasi-linear form of the Gauss–Helmert model, representing quasi-linear EIV models. This made it possible to obtain a linear disturbance/response relationship needed in that approach. Several specific cases of quasi-linear EIV models are discussed. The derived formulas are consistent with those already functioning for standard least squares adjustment problems. The analysis shows that, as could be expected, the average level of response-based reliability for such EIV models under investigation is lower than that for the corresponding standard linear models. For EIV models with homoscedastic and uncorrelated observations, the relationship between the average reliability indices for the independent and the dependent variables is formulated for multiple regression and coordinate transformations. Numerical examples for these two applications are provided to illustrate this analysis.