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

Authorized Users Only
2020
Authors
Božić, Branko
Contributors
Tucikesic, Sanja
Mulic, Medzida
Article (Published version)
,
Sanja Tucikesić
Metadata
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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 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 series
Source:
Tehnički vjesnik, 2020, 4, 27, 1229-1236
Publisher:
  • Strojarski fakultet u Slavonskom Brodu

DOI: 10.17559/TV-20190625140656

ISSN: 1330-3651

WoS: 000560780800027

Scopus: 2-s2.0-85090171704
[ Google Scholar ]
URI
https://grafar.grf.bg.ac.rs/handle/123456789/2102
Collections
  • Radovi istraživača / Researcher's publications
  • Катедра за геодезију и геоинформатику
Institution/Community
GraFar
TY  - 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 . .

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