Analysis of the time series of coordinates is extremely important in geodynamic research. Indeed, the correct interpretation of coordinate changes may facilitate an understanding of the diverse geophysical processes taking place in the earth’s crust. At present, when rigorously processing global navigation satellite system (GNSS) observations, the influence of deformations in the surface of the earth’s crust is not considered. This article presents signal modelling for the influence on the analysis of noise occurring in the time series of GNSS station coordinates. The modelling of coordinate time series was undertaken using the classic least-squares estimation (LSE) method and the inverse continuous wavelet transform (CWT). In order to determine the type of noise character, the coefficient spectral index was used. Analyses have demonstrated that the nature of noise in measurement data does not depend on the signal estimation method. The differences between classic modelling (LSE) of the time series with annual and semiannual oscillation and signal reconstruction are very small ( Δ κ = 0.0 ÷−0.2).

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classic leastsquares estimation lse method measurement data geodynamic research coefficient spectral index signal reconstruction annual and semiannual oscillation modelling of coordinate time series processing global navigation satellite system gnss inverse continuous wavelet transform earthrsquos crust

Adrian Kaczmarek,Bernard Kontny,.Identification of the Noise Model in the Time Series of GNSS Stations Coordinates Using Wavelet Analysis. 10 (10),.

Adrian Kaczmarek,Bernard Kontny,"Identification of the Noise Model in the Time Series of GNSS Stations Coordinates Using Wavelet Analysis" 10

Adrian Kaczmarek,Bernard Kontny,"Identification of the Noise Model in the Time Series of GNSS Stations Coordinates Using Wavelet Analysis"