## 5 Discussion  
Smartphone positioning has shown limitations in the accuracy of its results compared to those obtained with traditional geodetic instruments. However, the aim of this work was not to compare smartphones with high-performance GNSS geodetic instruments, but rather to define possible smartphone applications. It should be pointed out that although the smartphone is equipped with hardware for dual frequency carrier phase acquisition, this is not stored in the app used in the experimentation. Consequently, the measurements were carried out with only the information of the codes present in the various constellations. The lack of information of the phase measurement could be due to limitations imposed by the Android operating system.  
Tests with the three smartphones showed no substantial differences in accuracy and precision. The positioning errors of the three devices were processed for observation times of one, five and ten minutes, and the SPP and DGNSS solutions were fully compared. In particular, for the Huawei and OPPO smartphones, at one minute, there were errors in the order of meters and the positioning error decreased when the observation increased to five and to thirty minutes. Comparing the positioning errors at 60 min, it was observed that SPP is more advantageous than DGNSS in determining the Easting coordinate, unlike DGNSS, which is preferable to SPP for Up positioning. In Appendix C (Figure 11), it is possible to see a spatial distribution of the error obtained for SPP and DGNSS.  
In particular, the best planimetric positioning was obtained for the SPP sub-datasets whose outliers were eliminated with the Tukey method; the mean of Easting and Northing regions were equal to \(\Delta\)E\({}_{\text{Mn}}\) = 0.439 m and \(\Delta\)N\({}_{\text{Mn}}\) = 1.984. Good results were also obtained with the sub-datasets whose outliers were eliminated by clustering: in this case, the mean of Easting and Northing positioning errors were equal to \(\Delta\)E\({}_{\text{Kn}}\) = 0.706 m and \(\Delta\)N\({}_{\text{Kn}}\) = 1.984 m. The sub-datasets not treated with the elimination of the outliers instead showed mean positioning errors equal to \(\Delta\)E = 0.700 m and \(\Delta\)N\({}_{\text{Mn}}\) = 2.509 m. The best Up positioning was obtained with the DGNSS method, with a position error \(\Delta\)U\({}_{\text{Kn}}\) = 3.57 m; the other two sub-datasets \(\Delta\)U and \(\Delta\)U\({}_{\text{Mn}}\) instead obtained a mean positioning error equal to 3.58 m.  
The study of linear regressions shows that the accuracy and precision of SPP improves as the observation time increases, which leads the user to choose this mode if the observation can take place over a long period of time. In the DGNSS, no direct correlation between observation time and accuracy was found.  
The choice in location of the smartphones was made to use very short baselines (less than 1 km) in order to assess only the fixed amount of error without considering the variation due to the distribution of error as a function of distance. It was verified, considering another permanent GNSS station with baselines of about 50 km (Avetrana site), that DGNSS positioning errors do not vary significantly for positioning with code pseudoranges.  
Figure 5: Linear regression between \(\Delta\)E, \(\Delta\)N and \(\Delta\)U SPP error positioning and observation time.  
In order to test the applicability of the results obtained, a smartphone survey of the wall of a small amphitheatre was carried out. The survey consisted of the acquisition of four vertices of the amphitheatre with the Xiaomi Mi 10 (Figure 6) and observation times of 1 min with the Geo++ Rinex Logger for subsequent post processing in SPP. At the same time, the same vertices were acquired in real time with the Global Mapper Mobile app and with the same acquisition times. In Table 13 and Figure 6, it can be observed that SPP obtained better positioning results in 100% of the cases, proving to be better than Global Mapper Mobile positioning in real time.