M-estimator for the 3D symmetric Helmert coordinate transformation |
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| Authors: | Guobin Chang Tianhe Xu Qianxin Wang |
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| Institution: | 1.School of Environmental Science and Spatial Informatics,China University of Mining and Technology,Xuzhou,China;2.Institute of Space Science,Shandong University,Weihai,China;3.State Key Laboratory of Geo-information Engineering,Xi’an Research Institute of Surveying and Mapping,Xi’an,China |
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| Abstract: | The M-estimator for the 3D symmetric Helmert coordinate transformation problem is developed. Small-angle rotation assumption is abandoned. The direction cosine matrix or the quaternion is used to represent the rotation. The \(3 \times 1\) multiplicative error vector is defined to represent the rotation estimation error. An analytical solution can be employed to provide the initial approximate for iteration, if the outliers are not large. The iteration is carried out using the iterative reweighted least-squares scheme. In each iteration after the first one, the measurement equation is linearized using the available parameter estimates, the reweighting matrix is constructed using the residuals obtained in the previous iteration, and then the parameter estimates with their variance-covariance matrix are calculated. The influence functions of a single pseudo-measurement on the least-squares estimator and on the M-estimator are derived to theoretically show the robustness. In the solution process, the parameter is rescaled in order to improve the numerical stability. Monte Carlo experiments are conducted to check the developed method. Different cases to investigate whether the assumed stochastic model is correct are considered. The results with the simulated data slightly deviating from the true model are used to show the developed method’s statistical efficacy at the assumed stochastic model, its robustness against the deviations from the assumed stochastic model, and the validity of the estimated variance-covariance matrix no matter whether the assumed stochastic model is correct or not. |
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