Weighted Total Least Squares Method for Joint Adjustment Model with Weight Scaling Factor

被引：0

作者：

Wang L. ^{[1
,2
]}

Yu H. ^{[2
,3
]}

机构：

[1] College of Geomatics, Shandong University of Science and Technology, Qingdao

[2] Faculty of Geomatics, East China University of Technology, Nanchang

[3] School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou

来源：

Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2019年 / 44卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Discriminate function; Joint adjustment; Weight scaling factor; Weighted total least squares;

D O I：

10.13203/j.whugis20170265

中图分类号：

学科分类号：

摘要：

In regard to the joint adjustment problem with different types of dataset, the functional model of each type of dataset is affected by random errors, which indicates the observation vector and coefficient matrix are not error-free. In this paper, the weight total least squares (WTLS) method is applied to joint adjustment model. An iterative WTLS method for joint adjustment model is derived, which uses the weight scaling factor to adjust the contribution of each type of dataset. In view of the determination of the weight scaling factor, more schemes are designed, which includes the minimum discrimination function method. The results show that the prior unit weight variance method and the total least squares variance component estimation (TLS-VCE) method have their limitations. When the prior information is inaccurate or the variance components are not estimable while using the TLS-VCE method, the minimum discriminate function method with [Formula is presented] as its discriminate function can achieve the relative effective results. © 2019, Research and Development Office of Wuhan University. All right reserved.

引用

页码：1233 / 1240

页数：7

共 24 条

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