Application of Wavelet Decomposition and Singular Spectrum Analysis to GNSS Station Coordinate Time Series

DAI Hailiang; SUN Fuping; JIANG Weiping; XIAO Kai; ZHU Xinhui; LIU Jing

doi:10.13203/j.whugis20190107

Volume 46 Issue 3

Mar. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(3): 371-380. > DOI: 10.13203/j.whugis20190107

DAI Hailiang, SUN Fuping, JIANG Weiping, XIAO Kai, ZHU Xinhui, LIU Jing. Application of Wavelet Decomposition and Singular Spectrum Analysis to GNSS Station Coordinate Time Series[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 371-380. DOI: 10.13203/j.whugis20190107

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Application of Wavelet Decomposition and Singular Spectrum Analysis to GNSS Station Coordinate Time Series

1.
Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
2.
GNSS Research Center, Wuhan University, Wuhan 430079, China

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The National Natural Science Foundation of China 41674042

The National Natural Science Foundation of China 41804018

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Author Bio:
DAI Hailiang, master, specializes in navigation space-time datum. E-mail: David_109@163.com
Corresponding author:
SUN Fuping, PhD, professor. E-mail: sun.fp@163.com
Received Date: May 28, 2019
Published Date: March 04, 2021

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Abstract

Abstract

Objectives In order to effectively extract useful information from time series of the global navigation satellite system(GNSS) sites, and improve the modeling accuracy of coordinate time series, this paper proposes a nonlinear motion modeling method combining wavelet decomposition and singular spectrum analysis. Experiments were carried out using global positioning system(GPS) vertical coordinate time series of 11 stations around the world from 1999 to 2018.
Methods Firstly, the coordinate time series is decomposed into different scales by wavelet decomposition.And then the singular spectrum analysis(SSA) is performed on the high-frequency part and the low-frequency part of each layer. Finally, combine the fitting value that is the fitting coordinate time series, and the fitting effect of the new method was evaluated.
Results The results show that, compared with the simple singular spectrum analysis method, the new method can extract useful information such as trend and period more accurately from the limited scale of the time series with noise. And reduce the partial period items in the singular spectrum analysis method to some extent, for example, the seasonal period item and the monthly period item are regarded as the probability of noise rejection, and the modeling accuracy is improved by 26%.
Conclusions The purpose is to propose a nonlinear motion modeling method based on wavelet decomposition and singular spectrum analysis, so as to improve the modeling accuracy of coordinate time series.
- wavelet decomposition,
- singular spectrum analysis(SSA),
- time series,
- nonlinear,
- contribution ratio

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Application of Wavelet Decomposition and Singular Spectrum Analysis to GNSS Station Coordinate Time Series

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