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Journal of Geodetic Science

Editor-in-Chief: Eshagh, Mehdi

Open Access
Online
ISSN
2081-9943
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Scaled weighted total least-squares adjustment for partial errors-in-variables model

J. Zhao
Published Online: 2016-12-16 | DOI: https://doi.org/10.1515/jogs-2016-0010

Abstract

Scaled total least-squares (STLS) unify LS, Data LS, and TLS with a different choice of scaled parameter. The function of the scaled parameter is to balance the effect of random error of coefficient matrix and observation vector for the estimate of unknown parameter. Unfortunately, there are no discussions about how to determine the scaled parameter. Consequently, the STLS solution cannot be obtained because the scaled parameter is unknown. In addition, the STLS method cannot be applied to the structured EIV casewhere the coefficient matrix contains the fixed element and the repeated random elements in different locations or both. To circumvent the shortcomings above, the study generalize it to a scaledweighted TLS (SWTLS) problem based on partial errors-in-variable (EIV) model. And the maximum likelihood method is employed to derive the variance component of observations and coefficient matrix. Then the ratio of variance component is proposed to get the scaled parameter. The existing STLS method and WTLS method is just a special example of the SWTLS method. The numerical results show that the proposed method proves to bemore effective in some aspects.

Keywords: maximum likelihood; partial errors-in-variable model; scaled total least squares; scaled weighted total least squares; weighted total least squares; variance component

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About the article

Received: 2016-08-19

Accepted: 2016-02-01

Published Online: 2016-12-16


Citation Information: Journal of Geodetic Science, Volume 6, Issue 1, ISSN (Online) 2081-9943, DOI: https://doi.org/10.1515/jogs-2016-0010.

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© 2016 J. Zhao. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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