Noise variance decomposition of regional GNSS network with missing observations using structured additive predictor

Abstract

Abstract This study evaluates noise variance characteristics at configured GNSS stations using a combination of Factor Analysis (FA), stacked Gauss Markov Random Field (GMRF), and Structured Additive Predictor (SAP). FA is employed to decompose the variance in CGNSS time series into station-specific and common components. Shorter correlation times are dominated by specific variance, which accounts for over 60% of the total variance, while longer correlation times are more influenced by common variance, contributing up to 40%. Prediction errors using stacked GMRF and SAP were reduced by 15% compared to standard time series models, highlighting the effectiveness of these methods for spatial data imputation and prediction. Additionally, our analysis revealed that the choice of geodetic GNSS processing software (GAMIT vs. BERNESE) has a minimal impact on variance characteristics, with differences below 5%. Some stations demonstrated serial correlation, indicative of colored noise patterns. The analysis of data from fifty reference receivers demonstrates the practical viability and effectiveness of the proposed methodologies, offering a robust approach that enhances both the accuracy and interpretability of GNSS time series analysis.

Keywords

• CME (common mode error)
• EM (expectation-maximization)
• FA (factor analysis)
• GMRF (Gauss Markov random field)
• PC (principal component)
• PPP (precise point positioning)