An uncombined triple-frequency user implementation of the decoupled clock model for PPP-AR

Abstract

Precise point positioning (PPP) has proved its capacity to provide centimetre-level position solutions in open sky environments. However, the technique still suffers from relatively long initial convergence times. Research has proved the potential of ambiguity resolution (AR) to reduce the convergence time and three main methods are used to perform AR: the fractional cycle bias method, the decoupled clock model (DCM) and the integer recovery clock method. This paper focuses on the DCM and expands it at the user side to better fit the current context. Seeing as multi-frequency processing is proving to improve PPP performance, the classical DCM model is extended from a combined dual-frequency model to an uncombined triple-frequency one. The user implementation is tested on 1400, 3-h-long datasets from global IGS stations for 1 week with the Galileo constellation in both static and kinematic modes. First, some of the model-specific parameters are plotted and the estimated receiver biases are visualized. Then, dual- and triple-frequency PPP-AR results are shown. In both frequency modes, the convergence time and accuracy of the float solutions are improved with AR. In the dual-frequency case, the 100-percentile mean convergence time reduces from 19 min for the float solution to 14 min for the fixed solution, and the horizontal root mean square error improves 2.7 to 1.1 cm. In the triple-frequency case, the convergence time reduces from 17.5 min for the float solution to 9.5 min for the fixed solution, and the accuracy improves from 2.6 to 1.0 cm. These results show a minimal improvement in the accuracy between the dual-frequency and triple-frequency AR solutions, and a significant 40–50% improvement in the convergence time. Future work includes applying these developments to multi-constellation PPP-AR, which would further reduce the convergence time.