Over the last few years, there has been a rising demand for sub-metre accuracy (and
higher) for navigation and surveying using signals from Global Navigation Satellite
Systems (GNSS). To meet this rising demand, many precise positioning techniques
and algorithms using the carrier-phase observable have been developed. Currently,
high accuracy Real-Time Kinematic (RTK) positioning is possible using relative or
differential techniques which require one GNSS user receiver and at least one other
as the reference (known) station within a certain distance from the user. Unlike these
conventional differential positioning techniques, Precise Point Positioning (PPP) is
based on processing carrier phase observations from only one GNSS receiver. This
is more cost-effective as it removes the need for reference receivers and therefore, is
not limited by baseline length. However, errors mitigated by ‘differencing’ in
conventional methods must be modelled accurately and reliably for PPP.
This thesis develops a PPP software platform in Matlab code and uses it to
investigate the state-of-the-art PPP algorithms and develop enhancements.
Specifically, it is well documented that conventional PPP algorithms suffer from long
convergence periods ranging from thirty minutes (for static users) to hours (for
dynamic users). Therefore, to achieve fast convergence, two approaches are
developed in this thesis. Firstly, a combination of the state-of-the-art GNSS error
models and new algorithms for measurement weighting, management of receiver
clock jumps and assignment of a dynamic covariance factor, are exploited. Secondly,
based on the results of the analysis of the quantitative relationships between the PPP
convergence and each of the residual measurement noise level and satellite
geometry, a strategy for the selection of satellites (GPS and GALILEO) for PPP is
developed and exploited. Tests using 24 hours of real data show that the two
developments above contribute to the realisation of static PPP positioning accuracies
of 40 cm (3D, 100%) within a convergence time of 20 minutes. Furthermore, based
on simulated data, the same accuracy is achieved in kinematic mode but within a
convergence time of one hour. These levels of performance represent significant
improvements over the state-of-the-art (i.e. convergence time of twenty minutes
instead of thirty for static users and one hour instead of hours for dynamic users).
The potential of the use of multiple frequencies from modernised GPS and GALILEO
on float ambiguity PPP is demonstrated with simulated data, and shown to have the
potential to offer significant improvement in the availability of PPP in difficult user
environments such as urban areas. Finally, the thesis addresses the potential
application of PPP for mission (e.g. safety critical) applications and the need for
integrity monitoring. An existing Carrier-phase Receiver Autonomous Integrity
Monitoring (CRAIM) algorithm is implemented and shown to have the potential to
protect PPP users against abnormally large errors.