General Total Least Squares Theory for Geodetic Coordinate Transformations
Source abstract
Abstract
Datum transformations are a fundamental issue in geodesy, Global Positioning System (GPS) science and technology, geographical information science (GIS), and other research fields. In this study, we establish a general total least squares (TLS) theory which allows the errors-in-variables model with different constraints to formulate all transformation models, including affine, orthogonal, similarity, and rigid transformations. Through the adaptation of the transformation models to the constrained TLS problem, the nonlinear constrained normal equation is analytically derived, and the transformation parameters can be iteratively estimated by fixed-point formulas. We also provide the statistical characteristics of the parameter estimator and the unit of precision of the control points. Two examples are given, as well as an analysis of the results on how the estimated quantities vary when the number of constraints becomes larger.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Statistical and numerical algorithms · Geochemistry and Geologic Mapping · Soil Geostatistics and Mapping
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China
References (22)
Show source-provided references
1. Fang, X. (2011). Weighted Total Least Squares Solutions for Applications in Geodesy. [Ph.D. Thesis, Leibniz University Hannover].
2. Weighted Total Least Squares: Necessary and sufficient conditions, fixed and random parameters10.1007/s00190-013-0643-2 ↗
3. On non-combinatorial weighted Total Least Squares with inequality constraints10.1007/s00190-014-0723-y ↗
4. An analysis of the Total least –squares problem10.1137/0717073 ↗
5. Solution of the weighted symmetric similarity transformations based on quaternions
6. Generalization of total least-squares on example of unweighted and weighted 2D similarity transformation10.1007/s00190-010-0408-0 ↗
7. On weighted total least-squares adjustment for linear regression10.1007/s00190-007-0190-9 ↗
8. Wang, B., Liu, C., Fang, X., and Chen, W.J. (2020). A universally efficient algorithm and precision assessment for seamless 3D similarity transformation. Meas. Sci. Technol.10.1088/1361-6501/ab7dee ↗
9. On symmetrical three-dimensional datum conversion10.1007/s10291-008-0100-5 ↗
10. Nonlinear analysis of the three-dimensional datum transformation [conformal group C7(3)]10.1007/s00190-002-0299-9 ↗
11. A quanternion-based geodetic datum transformation algorithm10.1007/s00190-006-0054-8 ↗
12. A dual quaternion algorithm of the Helmert transformation problem10.1186/s40623-018-0792-x ↗
13. Parameter estimation in 3D affine and similarity transformation: Implementation of variance component estimation10.1007/s00190-018-1119-1 ↗
14. On least-squares solution to 3D similarity transformation problem under Gauss–Helmert model10.1007/s00190-015-0799-z ↗
15. Seamless multivariate affine error-in-variables transformation and its application to map rectification10.1080/13658816.2012.760202 ↗
16. Data snooping algorithm for universal 3D similarity transformation based on generalized EIV model10.1016/j.measurement.2018.01.040 ↗
17. Weighted total least-squares with constraints: A universal formula for geodetic symmetrical transformations10.1007/s00190-015-0790-8 ↗
18. Optimal computation of 3-D similarity: Gauss–Newton vs. Gauss–Helmert10.1016/j.csda.2012.03.014 ↗
19. Total Least Squares Solution of Coordinate Transformation10.1179/003962607X165005 ↗
20. Koch, K.R. (1999). Parameter Estimation and Hypothesis Testing in Linear Models, Springer. [2nd ed.].10.1007/978-3-662-03976-2 ↗
21. Mikhail, E.M., Bethel, J.S., and McGlone, C.J. (2001). Introduction to Modern Photogrammetry, Wiley.
22. Felus, Y.A., and Schaffrin, B. (2005, January 7–11). Performing similarity transformations using the Errors-in-Variables Model. Proceedings of the ASPRS Annual Conference, Baltimore, MD, USA.