Noise Characteristics in Single and Multi-GNSS Precise Point Positioning with Ambiguity Resolution: A Comparative Analysis of GPS, GLONASS, and Galileo

In Precise Point Positioning (PPP) GNSS processing strategy, understanding the stochastic noise, particularly white plus power-law and white plus flicker noise, is essential for improving precision of coordinate estimates. In this study, we present an analysis of single-system and multi-GNSS PPP AR solutions using observations from the GPS, GLONASS and Galileo constellations. We employed three independent software packages, the Bernese GNSS Software v5.4 (BSW5.4), PRIDE-PPPAR (PRIDE) and GipsyX v2.1 (GX2.1), each employing their recommended set of products and processing settings, while attempting to keep settings as consistent as possible between the software packages and processing runs. We processed data from 50 globally distributed IGS stations, carefully selected for known quality and network geometry, for 2019.0 – 2023.5. In our recently showed that Galileo's single-GNSS solutions outperform GPS and GLONASS in precision (AGU2023 conference poster presentation). Combining GPS with Galileo provide the highest precision in coordinate estimates. This was confirmed through consistent results from three PPP-AR tools. We highlight the advantage of combining GPS and Galileo for superior GNSS positioning precision. In this study, we conduct a comprehensive noise and power spectra analysis of the position time series, focusing on solutions from GPS, GLONASS, and Galileo constellations and combinations. A comprehensive exploration was performed, initially delving into the precision of coordinate estimates through both single system (GPS, GLONASS, and Galileo) solutions and multi-GNSS solutions (including all possible binary and triple combinations) followed by an investigation of the noise characterstics using the Hector v2.0 software. We employed both power-law plus white noise and flicker plus white noise models to assess the behavior and amplitude of the different noises. Moreover, this research uses spectral analysis to emphasize power reduction strategies for periodic signals in single-system and multi-GNSS solutions.