A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis
PBN-AR
Instytucja
Wydział Inżynierii Lądowej i Geodezji (Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego)
Informacje podstawowe
Główny język publikacji
en
Czasopismo
PURE AND APPLIED GEOPHYSICS (25pkt w roku publikacji)
ISSN
0033-4553
EISSN
1420-9136
Wydawca
BIRKHAUSER VERLAG AG
DOI
URL
Rok publikacji
2018
Numer zeszytu
5
Strony od-do
1841-1867
Numer tomu
175
Identyfikator DOI
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Autorzy
Słowa kluczowe
en
Probabilistic principal component analysis
common mode error
GNSS
time series analysis
missing data.
Streszczenia
Język
en
Treść
For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals’ (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations’ velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.
Cechy publikacji
discipline:Geodezja i kartografia
discipline:Geodesy and cartography
Original article
Original article presents the results of original research or experiment.
Oryginalny artykuł naukowy
Oryginalny artykuł naukowy przedstawia rezultaty oryginalnych badań naukowych lub eksperymentu.
Inne
System-identifier
PBN-R:862160
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