## 4 Discussion  
In this study, the comparison between Galileo, GPS, and GLONASS satellite positioning systems was proposed for a kinematic survey. The GNSS data were acquired with a Leica(tm) GS14 receiver and compared with the output obtained by a Mobile Mapping System (MMS), implementing integrated high-performance GPS/INS measurements. In particular, as far as the authors know, this is the first work that uses a precise MMS trajectory for the assessment of the kinematic performances of the Galileo system.  
All the differential solutions were produced with the open-source set of libraries RTKLIB. Particularly, the RTKLIB CUI was used to simulate a reduced operational status for the GPS and GLONASS systems. Specifically, thanks to the RTKLIB CUI capabilities, it was possible, by using the Python programming language, to contemporarily execute many solutions. Indeed, the aim was to produce and compare, in sets of four, five, and six satellites, all the possible and real acquisition scenarios occurring during the survey.  
In the authors' opinion, this experiment can be considered as a preliminary stress test for the Galileo system to verify if it has the potentiality to overpass the performances of the previous systems.  
Despite the effort to produce a fair comparison, the limited amount of Galileo satellites and their geometrical configuration (Figure 3) put the Galileo system in a disadvantaged position with respect to the other two analyzed systems. This remains true even if the whole survey was planned to maximize the number of available Galileo satellites. Indeed, the displayed performance, especially from a planimetric point of view, cannot be justified only by the fact that the whole survey was organized to maximize the probability of Galileo acquisitions. This can also be understood by considering the filtering strategy adopted before the applications of Equation (6), and the results of the application of Equation (6), used to select the solutions that were compared. A first important result was represented by the presence of Galileo combinations able to pass the filtering process. This was necessary to build the next comparisons by applying the objective function. In fact, the aim of Equation (6) was to select only the best solutions (low deviations with respect to the MMS solution) and, among these, to choose the ones characterized by a high fix rate. Equation (6) was applied, without any other constraint, to the three analyzed constellations. However, the difference between Galileo and the other tested systems relied on the different number of possible combinations that, in some cases, was of two orders of magnitude (Table 3).  
For these reasons, the fact that the planimetric results are very encouraging should be considered, as stated by other researchers (e.g., [3; 4; 5; 10]), also in relation to the lower level of Galileo signal noise. From an altimetric point of view, the results were different. However, because of the lower level of statistical significance (not as clear as in the planimetric case) and the higher performance correlated to the increasing number of satellites shown in Table 4, there is the need to analyze more data to properly assess the altimetric performances. In the authors' opinion, the very small number of available combinations for the Galileo system and the non-uniform distribution in the visible satellite elevations were responsible for the lower altimetric performances.  
Another result of this work is related to the experimental use of the software RTKLIB. The results performed for many combinations show a high occurrence of good solutions achieved with the \"fix and hold\" method for the GPS and GLONASS constellations. In case of post-processed analysis, this result can be instrumentally time-saving for those interested in using the RTKLIB set of tools. The same cannot be said for the Galileo constellation because of the lower amount of available satellites.