# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
## 4 Discussion  
ZTD estimations were analyzed for two different case studies, considering two different applications: an online application (CSRS-PPP) and a free and open-source software (RTKLIB 2.4.3 b33), modified by the authors. Two different types of GNSS receiver were employed for each test site: a smartphone and a geodetic receiver. This latter was considered as a comparison to test and verify if the results obtained from the software could be regarded as reliable. Firstly, attention was focused on the Turin test site (TORI and TOSM GNSS stations); the differences of ZTD estimations were not negligible if the open-source software was considered, even for the TORI GNSS station. This behavior was even reflected in the coordinate estimations, reaching differences of about 1 m for each component after the initialization phase. It is important to highlight the fact that all analyses and comparisons were made after the convergence of the solution, which was reached in approximately 20 min. Considering the CSRS-PPP software, the performances were better than the previous case considering the positioning solutions, whereas nothing can be confirmed concerning the ZTD values because CSRS-PPP was not able to estimate ZTD and it provided ZTD values from the VMF model. Starting from these analyses, it seems that the processing software plays a crucial role not only in the positioning but also in the ZTD estimations. For this reason, another case study was selected, which also extended the duration of the measurement campaign, considering a time interval of about 7 h. Considering the CSRS-PPP software, also in the present case, the ZTD estimations for GESM (smartphone) were not estimated by the software due to the poor quality of observations collected in the RINEX file derived from the smartphone, whereas the software provided ZTD estimates for GENU (CORS). Considering the RTKLIB software, the performances were quite different; in this case, the maximum span between GENU and GESM ZTD estimates was about 15 cm (Figure 15), even excluding the convergence time. In order to better understand how the processing software impacts the solutions, a deep analysis was made, comparing the ZTD estimations obtained for GENU and GESM with RTKLIB (Figure 18), respectively. In Figure 21, a comparison between the ZTD values estimated by these two types of software and those computed by the EUREF service were made for GENU; from this, it was possible to notice that the estimations made using CSRS-PPP were more in accordance with the EUREF ones with respect to the estimates obtained with the modified version of the RTKLIB software. This does not mean the one software is better than the other, because it could be interesting to deeply understand which are the processing strategies for both types of software. If, for RTKLIB, it is possible to make changes in the processing parameters thanks to its open-source state, considering the online one (CSRS-PPP) only a few settings can be modified. Thus, it is not possible to deeply investigate the constraints applied. However, this research aims to verify if a ZTD estimation made using smartphone devices and free software is possible and feasible, and not to evaluate what is the best software in terms of performances. Our work was intended to show what can be obtained with these two free types of software and to test their performances dealing with smartphone data. In both cases, the differences were about 20 cm, even if, in the latter case, the trend was less stable than in the first one. Thus, even if the quality of the data collected and the characteristics of the receivers have great importance for the ZTD estimations, it has been demonstrated that the processing software plays a crucial role and can provide quite different results. Despite that, the ZTD estimations obtained from smartphones can be considered feasible and useful.