Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California
Autor: | Boye Zhou, Xiaohui Zhou, Zhao Li, Weiping Jiang, Jun Ma |
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Rok vydání: | 2018 |
Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Series (mathematics) business.industry Aerospace Engineering Astronomy and Astrophysics White noise 010502 geochemistry & geophysics Geodesy 01 natural sciences Noise Geophysics Amplitude Space and Planetary Science Position (vector) Linear regression Global Positioning System General Earth and Planetary Sciences Flicker noise business 0105 earth and related environmental sciences Mathematics |
Zdroj: | Advances in Space Research. 61:2521-2530 |
ISSN: | 0273-1177 |
Popis: | The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components. |
Databáze: | OpenAIRE |
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