scholarly journals Götterdämmerung over total least squares

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
G. Malissiovas ◽  
F. Neitzel ◽  
S. Petrovic
Keyword(s):  
Least Squares ◽  
Similarity Transformation ◽  
Functional Model ◽  
Iterative Solution ◽  
Equation System ◽  
Total Least Squares ◽  
Algebraic Equations ◽  
Adjustment Problems ◽  
Straight Line ◽  
Non Linear

AbstractThe traditional way of solving non-linear least squares (LS) problems in Geodesy includes a linearization of the functional model and iterative solution of a nonlinear equation system. Direct solutions for a class of nonlinear adjustment problems have been presented by the mathematical community since the 1980s, based on total least squares (TLS) algorithms and involving the use of singular value decomposition (SVD). However, direct LS solutions for this class of problems have been developed in the past also by geodesists. In this contributionwe attempt to establish a systematic approach for direct solutions of non-linear LS problems from a "geodetic" point of view. Therefore, four non-linear adjustment problems are investigated: the fit of a straight line to given points in 2D and in 3D, the fit of a plane in 3D and the 2D symmetric similarity transformation of coordinates. For all these problems a direct LS solution is derived using the same methodology by transforming the problem to the solution of a quadratic or cubic algebraic equation. Furthermore, by applying TLS all these four problems can be transformed to solving the respective characteristic eigenvalue equations. It is demonstrated that the algebraic equations obtained in this way are identical with those resulting from the LS approach. As a by-product of this research two novel approaches are presented for the TLS solutions of fitting a straight line to 3D and the 2D similarity transformation of coordinates. The derived direct solutions of the four considered problems are illustrated on examples from the literature and also numerically compared to published iterative solutions.

scholarly journals Weighted Total Least Squares (WTLS) Solutions for Straight Line Fitting to 3D Point Data

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1450
Author(s):  
Georgios Malissiovas ◽  
Frank Neitzel ◽  
Sven Weisbrich ◽  
Svetozar Petrovic
Keyword(s):  
Least Squares ◽  
Iterative Solution ◽  
Total Least Squares ◽  
Dispersion Matrix ◽  
Random Errors ◽  
Direct Solution ◽  
Weighted Total Least Squares ◽  
Straight Line ◽  
Point Data ◽  
Line Fitting

In this contribution the fitting of a straight line to 3D point data is considered, with Cartesian coordinates xi, yi, zi as observations subject to random errors. A direct solution for the case of equally weighted and uncorrelated coordinate components was already presented almost forty years ago. For more general weighting cases, iterative algorithms, e.g., by means of an iteratively linearized Gauss–Helmert (GH) model, have been proposed in the literature. In this investigation, a new direct solution for the case of pointwise weights is derived. In the terminology of total least squares (TLS), this solution is a direct weighted total least squares (WTLS) approach. For the most general weighting case, considering a full dispersion matrix of the observations that can even be singular to some extent, a new iterative solution based on the ordinary iteration method is developed. The latter is a new iterative WTLS algorithm, since no linearization of the problem by Taylor series is performed at any step. Using a numerical example it is demonstrated how the newly developed WTLS approaches can be applied for 3D straight line fitting considering different weighting cases. The solutions are compared with results from the literature and with those obtained from an iteratively linearized GH model.

scholarly journals Modifying Cadzow's algorithm to generate the optimal TLS-solution for the structured EIV-Model of a similarity transformation

2012 ◽  
Vol 2 (2) ◽  
pp. 98-106 ◽  
Author(s):  
B. Schaffrin ◽  
F. Neitzel ◽  
S. Uzun ◽  
V. Mahboub
Keyword(s):  
Least Squares ◽  
Similarity Transformation ◽  
Optimal Solution ◽  
Total Least Squares ◽  
Errors In Variables ◽  
Parameter Adjustment ◽  
Structured Errors

Modifying Cadzow's algorithm to generate the optimal TLS-solution for the structured EIV-Model of a similarity transformationIn 2005, Felus and Schaffrin discussed the problem of a Structured Errors-in-Variables (EIV) Model in the context of a parameter adjustment for a classical similarity transformation. Their proposal, however, to perform a Total Least-Squares (TLS) adjustment, followed by a Cadzow step to imprint the proper structure, would not always guarantee the identity of this solution with the optimal Structured TLS solution, particularly in view of the residuals. Here, an attempt will be made to modify the Cadzow step in order to generate the optimal solution with the desired structure as it would, for instance, also result from a traditional LS-adjustment within an iteratively linearized Gauss-Helmert Model (GHM). Incidentally, this solution coincides with the (properly) Weighted TLS solution which does not need a Cadzow step.

scholarly journals Total Least Squares Spline Approximation

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 462 ◽  
Author(s):  
Frank Neitzel ◽  
Nikolaj Ezhov ◽  
Svetozar Petrovic
Keyword(s):  
Least Squares ◽  
Spline Approximation ◽  
Functional Model ◽  
Total Least Squares ◽  
Deformation Monitoring ◽  
Random Errors ◽  
Laser Scanners ◽  
Engineering Geodesy ◽  
Continuous Curvature ◽  
Cubic Polynomials

Spline approximation, using both values y i and x i as observations, is of vital importance for engineering geodesy, e.g., for approximation of profiles measured with terrestrial laser scanners, because it enables the consideration of arbitrary dispersion matrices for the observations. In the special case of equally weighted and uncorrelated observations, the resulting error vectors are orthogonal to the graph of the spline function and hence can be utilized for deformation monitoring purposes. Based on a functional model that uses cubic polynomials and constraints for continuity, smoothness and continuous curvature, the case of spline approximation with both the values y i and x i as observations is considered. In this case, some of the columns of the functional matrix contain observations and are thus subject to random errors. In the literature on mathematics and statistics this case is known as an errors-in-variables (EIV) model for which a so-called “total least squares” (TLS) solution can be computed. If weights for the observations and additional constraints for the unknowns are introduced, a “constrained weighted total least squares” (CWTLS) problem is obtained. In this contribution, it is shown that the solution for this problem can be obtained from a rigorous solution of an iteratively linearized Gauss-Helmert (GH) model. The advantage of this model is that it does not impose any restrictions on the form of the functional relationship between the involved quantities. Furthermore, dispersion matrices can be introduced without limitations, even the consideration of singular ones is possible. Therefore, the iteratively linearized GH model can be regarded as a generalized approach for solving CWTLS problems. Using a numerical example it is demonstrated how the GH model can be applied to obtain a spline approximation with orthogonal error vectors. The error vectors are compared with those derived from two least squares (LS) approaches.

scholarly journals PERFORMING 3D SIMILARITY TRANSFORMATION WITH LARGE ROTATION ANGLES USING CONSTRAINED MULTIVARIATE TOTAL LEAST SQUARES

2020 ◽  
Vol 26 (4) ◽  
Author(s):  
Wuyong Tao ◽  
Xianghong Hua ◽  
Shaoquan Feng
Keyword(s):  
Least Squares ◽  
Similarity Transformation ◽  
Coefficient Matrix ◽  
Total Least Squares ◽  
Translation Vector ◽  
Large Rotation ◽  
Parameter Matrix ◽  
Transformation Parameters ◽  
Least Squares Algorithm ◽  
3D Similarity

Abstract: 3D similarity transformation is frequently encountered operation in the field of geodetic data processing, and there are many applications that involve large rotation angles. In previous studies, the errors of the coefficient matrix were usually neglected and a least squares algorithm was applied to calculate the transformation parameters. However, the coefficient matrix is composed of the point coordinates in source coordinate system, i.e., the coefficient matrix is also contaminated by errors. Therefore, a total least squares algorithm should be applied. In this paper, a new method is proposed to address the 3D similarity transformation problem with large rotation angles. Firstly, the scale factor and rotation matrix are put together as the parameter matrix to avoid the rank-defect problem. Then, the translation vector is removed and the multivariate model is constructed. Finally, the constraints are introduced according to the properties of the parameter matrix and the constrained multivariate total least squares algorithm is derived to obtain the transformation parameters. The experimental results show that the proposed method has a high computational efficiency.

Least Squares Based Transient Nonlinear Gas Path Analysis

2005 ◽  
Author(s):  
Tomas Gro¨nstedt
Keyword(s):  
Path Analysis ◽  
Least Squares ◽  
Optimal Estimation ◽  
Equation System ◽  
Differential Algebraic Equations ◽  
System Of Nonlinear Equations ◽  
Algebraic Equations ◽  
Transient Model ◽  
Nonlinear Transient ◽  
Least Squares Estimates

A method for computing least squares estimates for transient nonlinear gas path analysis is derived. The solution to the optimal estimation problem is found by solving a system of nonlinear equations. A single iteration of the equation system requires integrating an extended set of nonlinear ordinary differential algebraic equations (ODAE). The additional differential equations originate from the differentiation of the least squares expression used to define optimality. The numerical efficiency of the extended ODAE algorithm is assessed by comparing it to an optimization based method. To illustrate the derived estimation technique a complete model of the Frank Whittle W1 engine is given within the paper. An example of the implementation of the extended ODAE method is demonstrated in the framework of this model. The performance of the method is also discussed and evaluated on a full nonlinear transient model of the RM12 fighter engine.

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