A Method for Partial EIV Model with Correlated Observations

doi:10.11947/j.AGCS.2017.20160430

Abstract

Abstract: As an extended form of the errors-in-variables(EIV) model, partial errors-in-variables(Partial EIV) model has more advantages than the previous one, such as regular structure, simple solving method, which make it has a wide range of applications. Considering the situation that the correlation between the observations and elements in coefficient matrix is not taken into account in the existed algorithms derived from Partial EIV model, the non-repetitive random elements in the augmented matrix consisting of observation vector and coefficient matrix are extracted to build a more suitable partial EIV model. Based on this model, the special assumptions are extended to the general case where the observations are correlated, a new weighted total least squares(WTLS)algorithms is derived when the observations and elements in coefficient matrix are heteroscedastic and correlated. Through two examples, the algorithm proposed in this paper and the existed algorithms which consider the correlation of the observation in EIV model are compared and analyzed. Research shows that these algorithms can improve the calculation efficiency and more general, especially for the situation that coefficient matrix consists of constant elements and repeated elements.

Key words: total least squares, correlated observations, partial errors-in-variables, autoregression model

CLC Number:

P207

WANG Leyang, XU Guangyu, WEN Guisen. A Method for Partial EIV Model with Correlated Observations[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(8): 978-987.

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