A New Cooperative PPP-RTK System with Enhanced Reliability in Challenging Environments

Compared to the traditional PPP-RTK methods, cooperative PPP-RTK methods provide expandable service coverage and eliminate the need for a conventional expensive data processing center and the establishment and maintenance of a permanently deployed network of dense GNSS reference stations. However, current cooperative PPP-RTK methods suffer from some major limitations. First, they require a long initialization period before the augmentation service can be made available from the reference stations, which decreases their usability in practical applications. Second, the inter-reference station baseline ambiguity resolution (AR) and regional atmospheric model, as presented in current state-of-art PPP-RTK and network RTK (NRTK) methods, are not utilized to improve the accuracy and service coverage of the network augmentation. Third, the positioning performance of current PPP-RTK methods would be significantly degraded in challenging environments due to multipath effects, non-line-of-sight (NLOS) errors, poor satellite visibility and geometry caused by severe signal blockages. Finally, current position domain or ambiguity domain partial ambiguity resolution (PAR) methods suffer from high false alarm and miss detection, particularly in challenging environments with poor satellite geometry and observations contaminated by NLOS effect, gross errors, biases, and high observation noise. This thesis proposed a new cooperative PPP-RTK positioning system, which offers significant improvements to provide fast-initialization, scalable coverage, and decentralized real-time kinematic precise positioning with enhanced reliability in challenging environments. The system is composed of three major components. The first component is a new cooperative PPP-RTK framework in which a scalable chain of cooperative static or moving reference stations, generates single reference station-derived or reference station network-derived state-space-representation (SSR) corrections for fast ambiguity resolution at surrounding user stations with no need for a conventional expensive data processing center. The second component is a new multi-feature support vector machine (SVM) signal classifier based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. The third component is a new PAR method based on machine learning, which employs the combination of two support vector machine (SVM) to effectively identify and exclude bias sources from PAR without relying on satellite geometry. The prototype of the new PPP-RTK system is developed and substantially tested using publically available real-time SSR products from International GNSS Service (IGS) Real-Time Service (RTS)