一种Partial EIV半参数模型的系统误差处理方法

在使用总体最小二乘求解参数时,若观测值中包含系统误差,此时得到的参数估值则会受到系统误差的影响,从而得到不可靠的解,因此必须削弱系统误差对参数估计的影响,以获得相对可靠的解。本文提出在partial errors-in-variables（Partial EIV）模型的基础上给观测值增加非参数部分（系统误差）,从而构建Partial EIV半参数模型;基于补偿最小二乘准则进行公式推导,并分别通过选取适当的正则化矩阵及通过L曲线法确定平滑因子。通过算例结果分析表明,与传统方法相比,本文的方法在一定程度上能够削弱系统误差的影响,得到更为可靠的参数解,从而验证了该方法的有效性和可行性。...

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Bibliographic Details
Published in测绘学报 Vol. 47; no. 1; pp. 25 - 34
Main Author 王乐洋;熊露雲
Format Journal Article
LanguageChinese
Published 江西省数字国土重点实验室,江西南昌330013%东华理工大学测绘工程学院,江西南昌330013 2018
东华理工大学测绘工程学院,江西南昌330013
流域生态与地理环境监测国家测绘地理信息局重点实验室,江西南昌330013
Subjects
EIV半参数模型
L曲线
Partial
平滑因子
正则化矩阵
系统误差
正则化矩阵
L-curve
平滑因子
regularization matrix
L曲线
systematic errors
smoothing factor
系统误差
Partial EIV半参数模型
Partial EIV semi-parametric model
Online AccessGet full text
ISSN1001-1595
DOI10.11947/j.AGCS.2018.20160613

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Summary:在使用总体最小二乘求解参数时,若观测值中包含系统误差,此时得到的参数估值则会受到系统误差的影响,从而得到不可靠的解,因此必须削弱系统误差对参数估计的影响,以获得相对可靠的解。本文提出在partial errors-in-variables（Partial EIV）模型的基础上给观测值增加非参数部分（系统误差）,从而构建Partial EIV半参数模型;基于补偿最小二乘准则进行公式推导,并分别通过选取适当的正则化矩阵及通过L曲线法确定平滑因子。通过算例结果分析表明,与传统方法相比,本文的方法在一定程度上能够削弱系统误差的影响,得到更为可靠的参数解,从而验证了该方法的有效性和可行性。
Bibliography:11-2089/P
WANG Leyang;XIONG Luyun;Faculty of Geomatics,East China University of Technology;Key Laboratory of Watershed Ecology and Geographical Environment Monitoring,NASG;Key Laboratory for Digital Land and Resources of Jiangxi Province
The estimated values are affected so that they are not reliable if the observations contain systematic errors under the solution of total least squares.Thus the bad effect on the estimated values should be weakened so the relatively reliable solution can be obtained.This paper adds non parametric part（systematic errors）in the partial errors-in-variables（Partial EIV）model,building Partial EIV semi-parametric model.The penalized least square criterion is introduced to derive formula,and choose the proper regularization matrix in the experiments.The smoothing factor is acquired by the method of L-curve.The results of experiments show that the algorithm of Partial EIV semi-parametric model can mitigate systematic errors to a certain extent and obtain the more reliable solution than
ISSN:1001-1595
DOI:10.11947/j.AGCS.2018.20160613