Weighted Total Least-Squares (WTLS) can optimally solve the issue of parameter estimation in the Errors-In-Variables (EIV) model; however, this method is relatively sensitive to outliers that may exist in the observation vector and/or the coefficient matrix. Hence, an attempt to identify/suppress those outliers is in progress and will ultimately lead to a novel robust estimation procedure similar to the one used in the Gauss-Markov model. The method can be considered as a follow-up to the WTLS solution formulated with the standard Least-Squares framework. We utilize the standardized total residuals to construct the equivalent weights, and apply the median method to obtain a robust estimator of the variance to provide good robustness in the observation and structure spaces. Moreover, a preliminary analysis for the robustness of related estimators within the EIV model is conducted, which shows that the redescending M-estimates are more robust than the monotonic ones. Finally, the efficacy of the proposed algorithm is demonstrated through two applications, i.e. 2D affine transformation and linear regression on simulated data and on real data with some assumptions. Unfortunately, the proposed algorithm may not be reliable for detecting multiple outliers. Therefore, MM-estimates within the EIV model need to be investigated in further research.

加权总最小二乘（WTLS）可以最佳地解决变量误差（EIV）模型中的参数估计问题。但是，该方法对观察矢量和/或系数矩阵中可能存在的离群值相对敏感。因此，正在进行识别/抑制这些离群值的尝试，并将最终导致一种新颖的鲁棒估计程序，该程序类似于高斯-马尔可夫模型中使用的估计程序。该方法可以看作是使用标准的最小二乘框架制定的WTLS解决方案的后续措施。我们利用标准化的总残差构造等效权重，并应用中位数方法获得方差的鲁棒估计量，以在观测和结构空间中提供良好的鲁棒性。而且，对EIV模型中相关估计量的鲁棒性进行了初步分析，结果表明，递减的M估计量比单调估计量更健壮。最后，该算法的有效性通过两个应用进行了证明，即在某些假设下对模拟数据和真实数据进行2D仿射变换和线性回归。不幸的是，提出的算法对于检测多个离群值可能并不可靠。因此，EIV模型中的MM估计需要进一步研究。在某些假设下，对模拟数据和真实数据进行2D仿射变换和线性回归。不幸的是，提出的算法对于检测多个离群值可能并不可靠。因此，EIV模型中的MM估计需要进一步研究。在某些假设下，对模拟数据和真实数据进行2D仿射变换和线性回归。不幸的是，提出的算法对于检测多个离群值可能并不可靠。因此，EIV模型中的MM估计需要进一步研究。