PPP-AR with GPS and Galileo: Assessing diverse approaches and satellite products to reduce convergence time

Precise Point Positioning (PPP) is one of the most promising processing techniques for Global Navigation Satellite System (GNSS) data. By the use of precise satellite products (orbits, clocks and biases) and sophisticated algorithms applied on the observations of a multi-frequency receiver, coordinate accuracies at the decimetre/centimetre level for a float solution and at the centimetre/millimetre level for a fixed solution can be achieved. In contrast to relative positioning methods (e.g. RTK), PPP does not require nearby reference stations or a close-by reference network. On the other hand PPP has a non-negligible convergence time. To make PPP more competitive against other high-precision GNSS positioning techniques, scientific research focuses on reducing the convergence time of PPP.

 

In this contribution, we present results of PPP with focus on integer ambiguity resolution (PPP-AR) using satellite products from different analysis centers. The resulting coordinate accuracy and convergence behaviour are evaluated in various test scenarios. In these test cases we distinguish between the use of satellite products from Graz University of Technology, which are calculated using a raw observation approach, and nowadays publicly available satellite products of different analysis centers (e.g. CNES, CODE). All those products enable PPP-AR in different approaches. To shorten the convergence time, we investigate and compare different PPP processing approaches using GPS and Galileo observations. The use of 2+ frequencies and alternatives to the classical PPP model, which is based on two frequencies and the ionosphere-free linear combination are discussed (e.g. uncombined model with ionospheric constraint). The PPP calculations are performed with the in-house software raPPPid, which has been developed at the research division Higher Geodesy of TU Vienna and is part of the Vienna VLBI and Satellite Software (VieVS PPP).