Impact of using type mean vs. individual receiver antenna PCC in multi-GNSS PPP

Abstract

AbstractIn the paper, the differences between position components obtained using the precise point positioning (PPP) technique with individual and type mean PCC models were investigated. Daily GNSS observations from ten selected European Permanent Network (EPN) stations were used in the study. Eight different combinations of GPS, Galileo, BeiDou and GLONASS systems observations were proposed for analyses. The observation processing was done using the open-source software package GAMP. The results show that differences in the calibration models propagate to the position domain. Position offsets, resulting from the use of individual calibrations instead of the type mean, reach up to 10 mm in the vertical component while are generally keeping below 2 mm in the horizontal ones. The analysis also shows that adapting GPS PCCs to other GNSS (e.g. Galileo or BeiDou) signals does not significantly increase achieved position component differences.Keywords: multi-GNSSEPNantenna phase center correctionsposition components time series‌PPP AcknowledgmentThe author is grateful for GNSS data provided by the International GNSS Service, EUREF Permanent Network, and Zhou et al. (Citation2018) for the free sharing of GAMP software.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKarol DawidowiczKarol Dawidowicz received his Ph.D. in Satellite Geodesy from the University of Warmia and Mazury in Olsztyn in 2001. His research interests cover GNSS-levelling, GNSS antenna phase center variations modeling and Precise Point Positioning. He is a member of the Advanced Methods of GNSS Data Processing Group.Mieczysław BakułaMieczysław Bakuła received his Ph.D. in Satellite Geodesy in 1999. His research has been focused on the positioning of global navigation satellite systems and their applications in surveying and navigation. He is the author of the Precise and Fast Method of Ambiguity Resolution/Reinitialization (PREFMAR).