GPS Stochastic Modelling
Global Navigation Satellite Systems (GNSS), such as GPS, have become an efficient, reliable and standard tool for a wide range of applications. However, when processing GNSS data, the stochastic model characterising the precision of observations and the correlations between them is usually simplified and incomplete, leading to overly optimistic accuracy estimates.
This work extends the stochastic model using signal-to-noise ratio (SNR) measurements and time series analysis of observation residuals. The proposed SNR-based observation weighting model significantly improves the results of GPS data analysis, while the temporal correlation of GPS observation noise can be efficiently described by means of autoregressive moving average (ARMA) processes. Furthermore, this work includes an up-to-date overview of the GNSS error effects and a comprehensive description of various mathematical methods.
Outstanding doctoral thesis nominated for a Springer Theses Prize by Karlsruhe Institute of Technology, GermanyThis work is a key step towards a realistic GNSS stochastic model, and provides good examples of statistical verification and physical interpretation of resultsThis thesis gives an up-to-date overview of the GNSS error effects and a comprehensive description of the mathematical modelsVarious statistical tests and methods of time series analysis are included, which can be applied in other research fields