Effects of errors-in-variables on the internal and external reliability measures

作     者：Yanxiong Liu Yun Shi Peiliang Xu Wenxian Zeng Jingnan Liu Yanxiong Liu;Yun Shi;Peiliang Xu;Wenxian Zeng;Jingnan Liu

作者机构：First Institute of OceanographyMinistry of Natural ResourcesQingdao 266061China School of GeomaticsXi'an University of Science and TechnologyXi'an 710048China Disaster Prevention Research InstituteKyoto UniversityUjiKyoto 611-0011Japan School of Geodesy and GeomaticsWuhan UniversityWuhan 430079China Research Center of GNSSWuhan UniversityWuhan 430079China 

出 版 物：《Geodesy and Geodynamics》 (大地测量与地球动力学（英文版）)

年 卷 期：2024年第15卷第6期

页      面：568-581页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：supported by the National Natural Science Foundation of China, Project No. 42174045 under National Key Research and Development Program of China, Project No.2020YFB0505805 the National Natural Science Foundation of China, Project No. 41874012 

主　　题：Weighted least squares Errors-in-variables model Nonlinear adjustment Total least squares Reliability theory 

摘      要：The reliability theory has been an important element of the classical geodetic adjustment theory and methods in the linear Gauss-Markov model. Although errors-in-variables(EIV) models have been intensively investigated, little has been done about reliability theory for EIV models. This paper first investigates the effect of a random coefficient matrix A on the conventional geodetic reliability measures as if the coefficient matrix were deterministic. The effects of such geodetic internal and external reliability measures due to the randomness of the coefficient matrix are worked out, which are shown to depend not only on the noise level of the random elements of A but also on the values of parameters. An alternative, linear approximate reliability theory is accordingly developed for use in EIV models. Both the EIV-affected reliability measures and the corresponding linear approximate measures fully account for the random errors of both the coefficient matrix and the observations, though formulated in a slightly different way. Numerical experiments have been carried to demonstrate the effects of errors-in-variables on reliability measures and compared with the conventional Baarda s reliability measures. The simulations have confirmed our theoretical results that the EIV-reliability measures depend on both the noise level of A and the parameter values. The larger the noise level of A, the larger the EIV-affected internal and external reliability measures;the larger the parameters,the larger the EIV-affected internal and external reliability measures.