Acta Geodaetica et Geophysica

, Volume 48, Issue 3, pp 293–304 | Cite as

Pareto optimality solution of the Gauss-Helmert model

Article

Abstract

The Pareto optimality method is applied to the parameter estimation of the Gauss-Helmert weighted 2D similarity transformation assuming that there are measurement errors and/or modeling inconsistencies.

In some cases of parametric modeling, the residuals to be minimized can be expressed in different forms resulting in different values for the estimated parameters. Sometimes these objectives may compete in the Pareto sense, namely a small change in the parameters can result in an increase in one of the objectives on the one hand, and a decrease of the other objective on the other hand. In this study, the Pareto optimality approach was employed to find the optimal trade-off solution between the conflicting objectives and the results compared to those from ordinary least squares (OLS), total least squares (TLS) techniques and the least geometric mean deviation (LGMD) approach.

The results indicate that the Pareto optimality can be considered as their generalization since the Pareto optimal solution produces a set of optimal parameters represented by the Pareto-set containing the solutions of these techniques (error models). From the Pareto-set, a single optimal solution can be selected on the basis of the decision maker’s criteria. The application of Pareto optimality needs nonlinear multi-objective optimization, which can be easily achieved in parallel via hybrid genetic algorithms built-in engineering software systems such as Matlab. A real-word problem is investigated to illustrate the effectiveness of this approach.

Keywords

Pareto optimality Gauss-Helmert transformation Parameter estimation Measurement and modeling errors Least squares approach Genetic algorithm 

References

  1. Akyilmaz O (2007) Total least squares solution of coordinate transformation. Surv Rev 39(303):68–80 CrossRefGoogle Scholar
  2. Felus YA, Schaffrin B (2005) Performing similarity transformations using the errors-in-variable model. In: ASPRS ann. conference, Baltimore, Maryland Google Scholar
  3. Golub GH, van Loan CF (1980) An analysis of the total least-squares problem. SIAM J Numer Anal 17(6):883–893 CrossRefGoogle Scholar
  4. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26:369–395 CrossRefGoogle Scholar
  5. Neitzel F (2010) Generalization of total least-squares on example of weighted 2D similarity transformation. J Geod 84(12):751–762. doi:10.1007/s00190-010-0408-0 CrossRefGoogle Scholar
  6. Tofallis C (2002) Model fitting for multiple variables by minimizing the geometric mean deviation. In: Van Hufferl S, Lemmerling P (eds) Total least squares and errors-in-variables modeling: algorithms, analysis and applications. Kluwer Academic, Dordrecht Google Scholar
  7. Tofallis C (2003) Multiple neutral data fitting. Ann Oper Res 124:69–79 CrossRefGoogle Scholar
  8. Völgyesi L, Tóth Gy, Varga J (1996) Conversion between Hungarian map projection systems. Period Polytech, Civ Eng 40(1):73–83 Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2013

Authors and Affiliations

  1. 1.Department of Photogrammetry and GeoinformaticsBudapest University of Technology and EconomyBudapestHungary
  2. 2.Department of Spatial SciencesCurtin UniversityBentleyAustralia
  3. 3.Department of Geodesy and SurveyingBudapest University of Technology and EconomyBudapestHungary

Personalised recommendations