An analysis of the mixed least squares-total least squares problems
Filomat, 2022 In this paper, we first get further consideration of the first order perturbation with normwise condition number of the MTLS problem. For easy estimation, we show a lower bound for the normwise condition number which is proved to be optimal. In order to overcome the problems encountered in calculating the normwise condition number, we give ...
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Total Least Squares In-Field Identification for MEMS-Based Inertial Measurement Units
RoboticsInertial Measurement Units are widely used in various applications and, hardware-wise, they primarily consist of a tri-axial accelerometer and a tri-axial gyroscope.
Massimo Duchi, Edoardo Ida’
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Consistency of the total least squares estimator in the linear errors-in-variables regression
Modern Stochastics: Theory and Applications, 2018 This paper deals with a homoskedastic errors-in-variables linear regression model and properties of the total least squares (TLS) estimator. We partly revise the consistency results for the TLS estimator previously obtained by the author [18]. We present
Sergiy Shklyar
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A Recursive Restricted Total Least-Squares Algorithm
IEEE Transactions on Signal Processing, 2014 We show that the generalized total least squares (GTLS) problem with a singular noise covariance matrix is equivalent to the restricted total least squares (RTLS) problem and propose a recursive method for its numerical solution. The method is based on the generalized inverse iteration. The estimation error covariance matrix and the estimated augmented
Stephan Rhode +3 more
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, 2022 Linear regression with shuffled labels and with a noisy latent design matrix arises in many correspondence recovery problems. We propose a total least-squares approach to the problem of estimating the underlying true permutation and provide an upper bound to the normalized Procrustes quadratic loss of the estimator.
Wang, Qian, Sussman, Daniel
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Linear-regression models and algorithms based on the Total-Least-Squares principle
Geodesy and Geodynamics, 2012 In classical regression analysis, the error of independent variable is usually not taken into account in regression analysis. This paper presents two solution methods for the case that both the independent and the dependent variables have errors.
Ding Shijun, Jiang Weiping, Shen Zhijuan
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An improved mixed total least squares method for strain inversion from distance changes
Geodesy and Geodynamics, 2016 Based on the deficiency of the traditional total least squares method (TLS) in the field of geodetic inversion, the mixed error characteristics of the errors in variables (EIV) model were analyzed by considering the distance azimuth measurement error in ...
Zhiping Liu, Sida Li, Hefang Bian
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Image Reconstruction Algorithm Based on Total Least Squares Target Correction for ECT. [PDF]
Comput Intell Neurosci, 2021 Wang L, Lv H, Chen D, Yang H, Li M.
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The Improvement Based on the DV-Hop Localization Algorithm for Wireless Sensor Networks
Journal of Harbin University of Science and Technology, 2018 As the problems of lower localization accuracy appeared in the traditional DV-Hop algorithm，the author analyzed three main factors that influence the localization accuracy of original DV-Hop algorithm which started from the calculation of the average ...
DONG Jing-wei +3 more
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Implications of Nonstationary Effect on Geographically Weighted Total Least Squares Regression for PM2.5 Estimation. [PDF]
Int J Environ Res Public Health, 2021 Mokhtari A, Tashayo B, Deilami K.
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