A Robust Weighted Total Least Squares Method
A Robust Weighted Total Least Squares Method
复制标题
DOI：
10.11947/j.agcs.2018.20180105
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Gong Xunqiang
中科院分区：
文献类型：
--
作者：
Gong Xunqiang
针对加权总体最小二乘法没有考虑观测数据粗差的问题，提出了一种加权总体最小二乘法的稳健重加权迭代法，通过大量的仿真数据和一组真实的-对基于Huber权函数的参数估计方法与加权总体最小二乘、稳健加权总体最小二乘进行了系统的比较，结果表明：1两种稳健加权总体最小二乘方法可以得到更可靠的参数估计; 2更重要的是，所提出的方法性能优于其他两种方法。
In weighted total least squares,gross errors of observation data are not taken into consideration.In order to resolve this problem,a robust method of weighted total least squares with reweighting iteration is proposed,which is based on IGG weight function.Thorough experimental evaluation with a large number of simulation datasets and a set of real-life data has been carried out.The results of parameter estimations are systematically compared with weighted total least squares,and robust weighted total least squares based on Huber weight function.It is shown that:1more reliable parameter estimations can be obtained by two robust weighted total least squares;2more importantly,the proposed method performs better than the two others.