# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
## 3 Results  
To gain an understanding of the performance of the current version of the RTKLIB with respect to Google's baseline solutions, both the RTKLIB and baseline solutions for the training set were assessed using the provided truth data. Prior to the analysis, training datasets with clock discontinuities were removed, as clock discontinuities suggest no valid carrier phase measurements for that epoch, which will throw off the proposed RTKLIB algorithm. If we look at these problematic datasets, whenever there were discontinuities, the position errors exceeded 100 m. Although the discontinuities exist for the GSDC test set as well, the aforementioned data merge mitigates this issue. We perform the exclusion for the train set analysis to look at the accuracy of the RTKLIB solutions prior to the merge. The excluded data and their number of discontinuities is provided in Table 2.  
After the data screening, the training data was separated into three scenarios: highway, street, and urban canyon. Highway indicates all the drives between San Francisco and San Jose, which are open-sky dominant. Urban canyon indicates the drives inside downtown San Jose, which are multipath dominant. Street indicates all the other drives, which have a mixture of both environments. An illustration of the three scenarios are provided in Figure 2. This classification is done to compare the absolute and relative performance of the RTKLIB with respect to the baseline solution. Figure 1: Availability visualization of all satellites present in the observation data for the Pixel 4 and Pixel 4 Modded. The left plot is for Pixel 4, and the right plot is for Pixel 4 Modded. The red markers indicate the flagged potential cycle slips present in the measurements. For the accuracy assessment, GSDC requires only the latitude and longitude coordinates. Therefore, in the training set assessment, the altitude for the solutions was set to be the same as the truth. This effectively allows us to attain only the horizontal error. When we look at the cumulative distribution function (CDF) for all the scenarios, as shown in Figure 3, it is clear that the RTKLIB solution was more accurate than Google's provided baseline solution in all scenarios. In addition, by merging the RTKLIB solutions, the accuracy of the final positions improved further. Overall, the baseline solution had 1.94 m for 50 th percentile and 10.29 m for the 95 th percentile, resulting in a score of 6.11. On the other hand, the merged RTKLIB solution attained 1.21 m for the 50 th percentile and 6.5 m for 95 th percentile, resulting in a score of 3.86. In terms of relative performance improvements, RTKLIB was the most effective over the baseline for the highway scenario. However, even for the absolute metrics, the unmerged RTKLIB provided almost 0.5 m better 50 th percentile and 1.5-5.1 m better 90 th percentile performance than the baseline. Furthermore, a limitation exposed by this CDF is that although RTKLIB is effective for open-sky conditions, the difficulty of mitigating multipath residuals remains. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario–Device** & **Total Epochs** & **Total Discontinuities** \\ \hline
2020-05-14-US-MTV-2\_Pixel4 XLModdded & 129 & 33 \\ \hline
2020-09-04-US-SF-1\_Pixel4 & 258 & 253 \\ \hline
2021-01-04-US-RWC-1\_Pixel4 Modded & 263 & 222 \\ \hline
2021-01-04-US-RWC-1\_Pixel4 XL & 272 & 272 \\ \hline
2021-01-04-US-RWC-2\_Pixel4 Modded & 262 & 262 \\ \hline
2021-01-04-US-RWC-2\_Pixel4 XL & 266 & 266 \\ \hline
2021-01-05-US-SVL-1\_Pixel4 XL & 263 & 222 \\ \hline
2021-01-05-US-SVL-1\_Pixel5 & 269 & 268 \\ \hline
2021-01-05-US-SVL-2\_Pixel4 XL & 257 & 197 \\ \hline
2021-03-10-US-SVL-1\_Pixel4 XL & 242 & 242 \\ \hline
2021-04-29-US-MTV-1\_Pixel4 & 235 & 121 \\ \hline
2021-04-29-US-MTV-1\_Pixel5 & 261 & 249 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scenario and device combinations for the datasets that were excluded due to the presence of hardware clock discontinuities.