BDS-3/GPS/Galileo OSB Estimation and PPP-AR Positioning Analysis of Different Positioning Models

Abstract

With the completion of the BeiDou Global Navigation Satellite System (BDS-3), the multi-system precise point positioning ambiguity resolution (PPP-AR) has been realized. The satellite phase fractional cycle bias (FCB) is a key to the PPP-AR. Compared to the combined ionosphere-free (IF) model, the undifferenced and uncombined (UDUC) model retains all the information from the observations and can be easily extended to arbitrary frequencies. However, the FCB is difficult to apply directly to the UDUC model. An observable-specific signal bias (OSB) can interact directly with the original observations, providing complete flexibility for PPP-AR for multi-frequency multi-GNSS. In this study, the OSB product generation for the GPS (G), Galileo (E), and BDS-3 (C) systems is performed using 117 globally distributed multi-GNSS experiment (MGEX) stations, and their performances are evaluated. Then, the PPP-AR comparison and analysis of the two positioning models of the UDUC and IF are conducted. The results show that the stability of OSB products of the three systems is better than 0.05 ns. For the precise point positioning (PPP) ambiguity fixed solution, with comparable positioning accuracy and convergence time to the products of both the Wuhan University (WUM) and the Centre National d’Etudes Spatials (CNES) institutions, an average fixed-ambiguity rate is over 90%. Compared to the PPP float solution, the PPP-AR has the most significant improvement in positioning accuracy in the E-direction. The average improvements in the positioning accuracy under the IF and UDUC models in the static and kinematic modes are higher than 45% and 40%, respectively. The convergence times of the IF and UDUC models are improved on average by 48% and 60% in the static mode and by 40% and 55% in the kinematic mode, respectively. Among the IF and UDUC positioning models, the former has slightly better positioning accuracy and convergence time than the latter for the PPP float solution. However, both models have comparable positioning accuracy and convergence time after the PPP-AR. The GCE multi-system combination is superior to other system combinations. The average convergence time for the static PPP fixed solution is 8.5 min, and the average convergence time for the kinematic PPP fixed solution is 16.4 min.

Algorithms Research and Precision Comparison of Different Frequency Combinations of BDS-3\GPS\Galileo for Precise Point Positioning in Asia-Pacific Region

“…The observation equation for GNSS pseudo-range observations and carrier phase observations can be written as follows [31,32]:…”

Influence of Stochastic Modeling for Inter-Frequency Clock Biases on Multi-Frequency Precise Point Positioning

“…The kinematic PPP performance was evaluated using indices such as positioning accuracy, convergence time, the third frequency phase residuals' root mean square (RMS) and estimated IFCB. The position was considered to have converged only when the positioning error (east, north, up) of the epoch and the subsequent 20 epochs were within a 1 dm threshold [34,35]. For convenience, the five schemes mentioned above are marked as IFCB_NO (Neglect), IFCB_PC (Product Correction), IFCB_CT (random constant), IFCB_WN (white noise), and IFCB_RW (random walk), respectively.…”

Improving Vehicle Positioning Performance in Urban Environment with Tight Integration of Multi-GNSS PPP-RTK/INS

“…It should be noted that other errors, such as phase center offsets (PCOs) and variations (PCVs), the relativistic effect, tidal load deformation (earth tide, polar tide, and sea tide), the Sagnac effect, and phase wind-up have been corrected in advance and are no longer shown in Equation (1) [34].…”