Data snooping algorithm for universal 3D similarity transformation based on generalized EIV model
Data snooping algorithm for universal 3D similarity transformation based on generalized EIV model
复制标题
基于广义EIV模型的通用3D相似变换数据窥探算法
DOI：
10.1016/j.measurement.2018.01.040
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发表时间：
2018-04
期刊：
影响因子：
5.6
通讯作者：
朱邦彦
中科院分区：
文献类型：
--
作者：
王彬;余洁;刘超;李明峰;朱邦彦
三维相似基准变换在大地测量等诸多领域有着广泛的应用。近年来，通用三维相似变换问题(任意旋转角度和比例)的总体最小二乘(TLS)解已成为研究热点，并提出了许多算法。然而，当观测坐标受到粗差污染时，估计的变换参数会受到影响，甚至严重失真。本文将三维相似变换问题描述为一个广义的变量误差模型，并提出了该模型的数据窥探算法。首先利用欧拉-拉格朗日方法推导出广义EIV模型的加权总体最小二乘解，然后将其转化为经典的最小二乘问题。基于经典的最小二乘理论，在方差分量已知和未知的情况下，构造了两种数据监听的检验统计量。实际和仿真实验结果表明，该算法能有效地降低粗差的影响，得到可靠的变换参数。
Three-dimensional (3D) similarity datum transformation is extensively applied in geodetic field and many other areas. In recent years, the total least squares (TLS) solution for universal 3D similarity transformation problem (with arbitrary rotation angles and scale ratio) has become a hot research issue and many algorithms have been proposed. However, the estimated transformation parameters are affected or even severely distorted when the observed coordinates are contaminated by gross errors. In this study, the 3D similarity transformation problem is described as a generalized errors-in-variables (EIV) model, and then the data snooping algorithm for this model is proposed. The weighted total least squares (WTLS) solution to the generalized EIV model is firstly derived through Euler–Lagrange method and then we reformulate it as a classical least squares problem. Two types of test statistics for data snooping are constructed based on the classical least squares theory under the conditions with known and unknown variance component, respectively. The results of the real and simulated experiments indicate that the proposed algorithm can effectively reduce the influence of the gross errors and obtain reliable transformation parameters.
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用于通用 3D 相似变换的广义总体最小二乘预测算法
影响因子：
2.6
作者：
王彬;李建成;刘超;余洁
通讯作者：
余洁
影响因子：
5.6
作者：
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A. Amiri-Simkooei;F. Zangeneh-Nejad;J. Asgari;S. Jazaeri
影响因子：
1.3
作者：
Amiri-Simkooei, A.;Jazaeri, S.
通讯作者：
Jazaeri, S.
影响因子：
8.2
作者：
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通讯作者：
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影响因子：
4.4
作者：
Yunzhong Shen;Bofeng Li;Yi Chen
通讯作者：
Yunzhong Shen;Bofeng Li;Yi Chen