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Remote Sens. 2018, 10(10), 1611; https://doi.org/10.3390/rs10101611

Identification of the Noise Model in the Time Series of GNSS Stations Coordinates Using Wavelet Analysis

Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wroclaw, Poland
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Received: 15 August 2018 / Revised: 19 September 2018 / Accepted: 9 October 2018 / Published: 10 October 2018
(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))
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Abstract

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). View Full-Text
Keywords: wavelet analysis; coordinate time series; noise; spectral index; Continous Wavelet Transform signal reconstruction wavelet analysis; coordinate time series; noise; spectral index; Continous Wavelet Transform signal reconstruction
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Kaczmarek, A.; Kontny, B. Identification of the Noise Model in the Time Series of GNSS Stations Coordinates Using Wavelet Analysis. Remote Sens. 2018, 10, 1611.

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