Absolute Time Series GNSS Point Positioning-Data Cleaning and Noise Characterization
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2020
Article (Published version)
,
Sanja Tucikesić
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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 lo...cated 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 seriesSource:
Tehnički vjesnik, 2020, 4, 27, 1229-1236Publisher:
- Strojarski fakultet u Slavonskom Brodu
DOI: 10.17559/TV-20190625140656
ISSN: 1330-3651
WoS: 000560780800027
Scopus: 2-s2.0-85090171704
Institution/Community
GraFarTY - JOUR AU - Božić, Branko PY - 2020 UR - https://grafar.grf.bg.ac.rs/handle/123456789/2102 AB - 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. PB - Strojarski fakultet u Slavonskom Brodu T2 - Tehnički vjesnik T1 - Absolute Time Series GNSS Point Positioning-Data Cleaning and Noise Characterization EP - 1236 IS - 27 SP - 1229 VL - 4 DO - 10.17559/TV-20190625140656 ER -
@article{ author = "Božić, Branko", year = "2020", 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.", publisher = "Strojarski fakultet u Slavonskom Brodu", journal = "Tehnički vjesnik", title = "Absolute Time Series GNSS Point Positioning-Data Cleaning and Noise Characterization", pages = "1236-1229", number = "27", volume = "4", doi = "10.17559/TV-20190625140656" }
Božić, B.. (2020). Absolute Time Series GNSS Point Positioning-Data Cleaning and Noise Characterization. in Tehnički vjesnik Strojarski fakultet u Slavonskom Brodu., 4(27), 1229-1236. https://doi.org/10.17559/TV-20190625140656
Božić B. Absolute Time Series GNSS Point Positioning-Data Cleaning and Noise Characterization. in Tehnički vjesnik. 2020;4(27):1229-1236. doi:10.17559/TV-20190625140656 .
Božić, Branko, "Absolute Time Series GNSS Point Positioning-Data Cleaning and Noise Characterization" in Tehnički vjesnik, 4, no. 27 (2020):1229-1236, https://doi.org/10.17559/TV-20190625140656 . .