Precise Point Positioning with Fast Ambiguity Resolution - Prerequisites, Algorithms and Performance

The PPPAR technique is providing higher positioning accuracy than standard PPP because resolving the ambiguities decreases the degree of freedom in the client solution. Similar to baseline processing with ambiguity resolution it is primarily the east-west component of the solution that is improved by ambiguity resolution. 1-cm level horizontal positioning of fully kinematic clients is possible with PPP - AR. This level of accuracy is needed for the most demanding applications in precision farming, deformation monitoring, GNSS seismology, and surveying. Many groups have been pursuing PPPAR (often also called PPP RTK) for years (Wubbena et al., 2005), (Ge et al., 2007), (Geng et al., 2009), (Collins et al., 2008), (Laurichesse et al., 2009), to name just a few. At GPS Solutions we have developed two improvements of the original PPPAR method that we presented first at the ION conference in Savannah, GA in 2008 see (Mervart et al., 2008). The first improvement is aimed to handle the fast (within seconds) ambiguity fixing after an interruption (gap) in receiver measurements. This approach uses the dual frequency observations of the client receiver to estimate the ionospheric delay in the direction of all observed satellites. The second approach deals with fast (within seconds or at most within a few minutes) ambiguity resolution after the receiver cold start. In this case additional ionospheric information has to be provided to the client. We model and estimate the ionosphere based on dual frequency observations from a regional GNSS reference network on the server side and transmit these corrections to the client for interpolation. The client determines a satellite specific ionospheric delay with these corrections and uses this delay as a constraint for rapid ambiguity fixing. We call this approach PPP with fast ambiguity resolution (PPPFAR). The presentation describes the principles of the two methods mentioned above, discusses the necessary prerequisites like the necessary density of the reference network, and shows the results achieved in both regional and global scales.