On weighted total least-squares for geodetic transformations

被引:1
作者
Vahid Mahboub
机构
[1] University of Tehran,Department of Surveying and Geomatics Engineering, Geodesy Division, Faculty of Engineering
来源
Journal of Geodesy | 2012年 / 86卷
关键词
EIV model; Weighted total least-squares principle; Similarity transformation; Affine transformation;
D O I
暂无
中图分类号
学科分类号
摘要
In this contribution, it is proved that the weighted total least-squares (WTLS) approach preserves the structure of the coefficient matrix in errors-in-variables (EIV) model when based on the perfect description of the dispersion matrix. To achieve this goal, first a proper algorithm for WTLS is developed since the quite recent analytical solution for WTLS by Schaffrin and Wieser is restricted to the condition \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{P}_{\rm A} =\left({P_0 \otimes P_x}\right)}$$\end{document} (where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\otimes}$$\end{document} is used to denote the Kronecker product) for the weight matrix of the coefficient matrix in the EIV model. This situation can be seen in the case of an affine transformation where the univariate approach can be an appropriate alternative to the multivariate WTLS approach, which has been applied to the affine transformation by Schaffrin and Felus, resp. Schaffrin and Wieser with restrictions similar to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{P}_{\rm A} =\left( {P_0 \otimes P_x}\right)}$$\end{document}. In addition, this algorithm for WTLS can be interpreted well in the geodetic literature since it is based on the perfect description of the inverse dispersion matrix (or variance–covariance). By using the algorithm of WTLS, one obtains more realistic results in some applications of transformation where a high precision is needed. Some empirical examples, resp. simulation studies give insight into the efficiency of the procedure.
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页码:359 / 367
页数:8
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共 18 条
[1]  
Cadzow JA(1988)Signal enhancement—a composite property mapping algorithm IEEE Trans Acoust Speech Signal Process 36 49-62
[2]  
Golub G(1980)An analysis of the total least squares problem SIAM J Num Anal 17 883-893
[3]  
van Loan C(2006)The element-wise weighted total least-squares problem Comput Stat Data Anal 50 181-209
[4]  
Markovsky I(2010)Generalizations of the total least squares problem Wiley Interdiscip Rev Comput Stat 2 212-217
[5]  
Rastello M(1989)An accurate and straightforward approach to line regression analysis of error-affected experimental data J Phys Ser E: Sci Instr 22 215-217
[6]  
Premoli A(2008)On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms J Geod 82 353-383
[7]  
Kukush A(2008)On weighted total least-squares adjustment for linear regression J Geod 82 415-421
[8]  
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[9]  
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[10]  
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