Original scientific paper

https://doi.org/10.17559/TV-20190625140656

Absolute Time Series GNSS Point Positioning-Data Cleaning and Noise Characterization

Sanja Tucikešić orcid.org/0000-0002-6049-6242 ; University of Banja Luka Faculty of Architecture, Civil Engineering and Geodesy, StepeStepanovića 77 /3, 78000 Banja Luka, Bosnia and Herzegovina
Branko Božić ; University of Belgrade Faculty of Civil Engineering, Bulevarkralja Aleksandra 73, 11000 Belgrade, Serbia
Medžida Mulić orcid.org/0000-0003-0651-3782 ; University of Sarajevo Faculty of Civil Engineering, Patriotskelige 30, 71000 Sarajevo, Bosnia and Herzegovina

Abstract

Time series data of GNSS point positioning are considerably used for the purpose of geophysical research. The velocity estimates and their uncertainties derive from time series data of GNSS point positioning affected by seasonal signals and the stochastic noise, contained in the series. Data cleaning of GNSS time series is a prerequisite for the noise characterization and analysing. In this article one point positioning of time series was analysed in four different periods during the five year interval. The noise characteristics were estimated for all periods. By applying Lomb-Scargle algorithm the comparable results were also provided. Lomb-Scargle algorithm used to estimate the spectral strength density of unequal sampled data is a typical tool for this kind of analysis. Spectral indices have been estimated before cleaning data and after removing linear, annual and semi-annual signals and outliers. The spectral indices estimated from time series data of GNSS point positioning were located in the area of fractional Gaussian noises, and stationary stochastic process was described for the whole research time period.

Keywords

GNSS; Lomb-Scargle algorithm; spectral indices; time series

Hrčak ID:

242326

URI

https://hrcak.srce.hr/242326

Publication date:

15.8.2020.

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