Phase multipath modelling and mitigation in multiple frequency GPS and Galileo positioning
Multipath is the main error source in short- to medium-baseline GNSS (Global Navigation Satellite System) relative positioning. So, in order to achieve the highest possible accuracy, multipath errors must be modelled and/or mitigated. A new era in GNSS positioning is on the horizon. GPS modernisation is being undertaken, which will provide an unencrypted civil signal (L2C) on the L2 frequency and the signal power of the L2 signal will be increased. Also an additional signal, the so-called L5, will be available on GPS Block IIF satellites scheduled for launch beginning in mid- 2006. Furthermore, the European GNSS, named Galileo, is being developed to provide four carrier frequencies and its Full Operational Capability (FOC) is scheduled to be in 2008, but more likely in 2010. This study identifies and models the factors causing phase multipath errors and investigates some possible phase multipath mitigation techniques using the multiple frequency data that modernised GPS and Galileo will offer. A GNSS data simulator has been developed to generate multipath contaminated data using a phase multipath model based on ray tracing. All known geometrical and physical factors have been taken into account and are described in detail. The model has been validated with real data collected in two experiments with reflectors of different materials. A GNSS data processor has been developed for this validation and for subsequent analyses. The results show good agreement (i.e. similar amplitude and frequency) with real multipath from a steel panel (planar reflector) and fairly good agreement (i.e. similar amplitude with slight different frequency) with real multipath from a lake (dynamic irregular reflector). They show that the multipath model has the potential to correct phase multipath errors in cases where the exact geometry of the reflection process and the nature of the reflector are known. Some of the characteristics of phase multipath and the sensitivities of simulated GNSS measurements to the factors causing multipath are investigated and described. Multipath mitigation through averaging based on the least squares process and standard outlier detection technique using multiple frequency GPS, Galileo, and integrated GPS and Galileo data have been investigated. Since multiple frequency GPS and Galileo data are not yet available, all data has been generated by the GNSS data simulator described in the foregoing. It was found that standard outlier detection techniques were not sufficiently robust to tackle the frequency-dependent multipath errors because they could not handle the worst case scenario when multiple frequency multipath errors from a particular satellite were all in-phase. Therefore a cocktail multiple outlier detection algorithm has been proposed and tested. Results show that a combination of more satellites, more frequencies and the cocktail multiple outlier algorithm can substantially mitigate multipath errors and so improve positioning accuracy.