The Real Time Kinematic (RTK) method is widely used in the land surveying. Whereas RTK method has the advantage of practical use, positioning accuracy depends mostly on the baseline length due to the atmospheric errors. In general, RTK measurements are made by using GPS and GLONASS satellite systems. For this reason, the positioning performance of the technique is adversely affected under restricted satellite geometry conditions such as urban canyons. At present, most receivers on the market have the ability to track signals of Galileo and BeiDou satellites. Therefore, in this study, the positioning performance of RTK with different satellite combinations (GPS-only, GPS+GLONASS, GPS+GLONASS+GALILEO+BeiDou) was examined with a comparative approach. A field test was carried out considering approximately 20, 40, 60, and 80 km length of baselines. Three different cut off elevation angles – namely, 10°, 20°, and 30° – were chosen for the field test. The results were investigated in terms of accuracy and precision. Also, the ground truth coordinates of the rovers were obtained by post-processing relative method using GAMIT/GLOBK software. The results showed that multi-GNSS combinations provided better repeatability at the 10° cut off angle option. The accuracy of GPS-only solutions varied between 0.63/2.17 cm and 2.40/4.94 cm for horizontal and vertical components, respectively. However, the multi-GNSS combinations did not have a remarkable superiority in terms of position accuracy even at high satellite cut off angle (30°) compared to the GPS-only RTK.

Precise point positioning with ambiguity resolution (PPP-AR) is a powerful tool for geodetic and time-constrained applications that require high precision. The performance of PPP-AR highly depends on the reliability of the correct integer carrier-phase ambiguity estimation. In this study, the performance of narrow-lane ambiguity resolution of PPP using the Least-squares AMBiguity Decorrelation (LAMBDA) and bootstrapping methods is extensively investigated using real data from 55 IGS stations over one-month in 2020. Static PPP with 24-, 12-, 8-, 4-, 2-, 1- and ½-h sessions using two different cutoff angles (7° and 30°) was conducted with three PPP modes: i.e. ambiguity-float and two kinds of ambiguity-fixed PPP using the LAMBDA and bootstrapping methods for narrow-lane AR, respectively. The results show that the LAMBDA method can produce more reliable results for 2 hour and shorter observation sessions compared with the bootstrapping method using a 7° cutoff angle. For a 30° cutoff angle, the LAMBDA method outperforms the bootstrapping method for observation sessions of 4 h and less. For long observation times, the bootstrapping method produced much more accurate coordinates compared with the LAMBDA method without considering the wrong fixes cases. The results also show that occurrences of fixing the wrong integer ambiguities using the bootstrapping method are higher than that of the LAMBDA method.

In the middle of 2011, Trimble introduced the RTX correction service to support RT-PPP applications. In this study, the performance of Trimble RTX correction service is investigated over a one-month period using five different GNSS constellations – namely, GPS-only, GPS + GLONASS, GPS + Galileo, GPS + GLONASS + Galileo, and GPS + GLONASS + Galileo + BeiDou. The results show that positioning accuracy and convergence time are significantly improved with the use of the multi-GNSS constellation compared with the GPS-only solution. The result of the converged positioning accuracy indicates that the GPS + GLONASS + Galileo + BeiDou combination improves the accuracy by 63%, 54%, and 60% for north, east, and up components, respectively, compared with the GPS-only solution. The mean convergence time is reduced by the GPS + GLONASS+ Galileo + BeiDou combination by 70%, 71%, and 38.6% compared with the GPS-only solution in the north, east, and up components, respectively. 1.2 (north), 1.5 (east), and 2.3 cm (up) root mean square errors (RMSEs) of converged positioning from the GPS + GLONASS + Galileo + BeiDou combination are computed.