Precise point positioning (PPP) allows for centimeter- to decimeter-level positioning accuracy
using a single global navigation satellite system (GNSS) receiver. However, the use of PPP is
presently limited due to the time required for the solution to converge or re-converge to the
expected accuracy, which typically requires about 30 minutes. This relatively long convergence
time is essentially caused by the existing un-modeled GNSS residual errors. Additionally, in
urban areas, the number of visible satellites is usually limited when a single satellite constellation
is used, which in turn slows down the PPP solution convergence. This, however, can be
overcome by combining the observations of two constellations, namely the GPS and Galileo
systems.
Unfortunately, combining the GPS and Galileo constellations, although enhances the satellite
geometry, introduces additional biases that must be considered in the observation mathematical
models. These include the GPS-to-Galileo time offset, and Galileo satellite and receiver
hardware delays. In addition, the stochastic characteristics of the new Galileo E1 and E5a signals
must be determined to a high degree of precision. This can be done by analyzing various sets of
GPS and Galileo measurements collected at two stations with short separation.
Several PPP models are developed in this dissertation, which combine GPS and Galileo
observations in the un-differenced and between-satellite single-difference (BSSD) modes. These
include the traditional un-differenced model, the decoupled clock model, the semi-decoupled
clock model, and the between-satellite single-difference model. It is shown that the traditional
un-differenced GPS/Galileo PPP model, the GPS decoupled clock model, and semi-decoupled
clock GPS/Galileo PPP model improve the convergence time by about 25% in comparison with
the un-differenced GPS-only PPP model. In addition, the semi-decoupled GPS/Galileo PPP
model improves the solution precision by about 25% compared to the traditional un-differenced
GPS/Galileo PPP model. Moreover, the BSSD GPS/Galileo PPP model improves the solution
convergence time by about 50%, in comparison with the un-differenced GPS PPP model,
regardless of the type of BSSD combination used. As well, the BSSD model improves the
solution precision by about 50% and 25% when the BSSD loose and tight combinations are used,
respectively, in comparison with the un-differenced GPS-only model.