## 6 Conclusions  
In this study, the accuracies of the horizontal and vertical positions provided by the NLP under various scenarios were investigated. When only the cell positioning was used, no altitude measurements were provided, and the horizontal accuracy was limited to 500 m or worse. When Wi-Fi was available, the urban scenario had the best horizontal accuracy, and the worst vertical accuracy. This is because in the urban case, there were the greatest number of access points available lending to the high horizontal accuracy. However, those access points were located on various floors of multi-story buildings, and the locations used for the crowdsourcing were also from various altitudes. Consequently, for rural environments, some level of coarse positioning is available from the cell locations when two or more cell towers were available. When one or more Wi-Fi access points registered in the fingerprint database is available, altitude measurements are available as well. For suburban navigation, the expected accuracy of the NLP is 38 m horizontal and 1.2 m vertical for 68% CEP. For urban navigation, the expected horizontal accuracy increases further but the altitude estimates are less reliable. Also, the Android estimated errors are similar to the actual errors for the suburban scenario, but the Android underestimates the horizontal accuracy for rural environments, and underestimates the vertical accuracy for urban environments. A summary of the NLP performances is provided in Table 4. However, it is important to note that these performances were based on the network fingerprinting database available at that time to the Android devices. If any of the access points are not available in the database, they cannot be used for navigation solution computation, and if the location of the access points has moved and has not been updated in the database yet, inaccurate localization solutions can be computed. Also, the NLP rate suggests that although it can be used for various navigation purposes, the relatively lower update rate and latency compared to other sensors, such as GNSS and INS, may be a limiting factor for high dynamic operations. This study provides insight into the expected accuracy of Android NLP under various environments, and assesses the accuracy of the corresponding estimated errors. Furthermore, the altitude analysis suggests that although NLP is often used for geolocation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{\begin{tabular}{c} **Rate** \\ **(seconds)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Access** \\ **Points** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Horiz.** \\ **Accuracy** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Vert.** \\ **Accuracy** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Horiz.** \\ **Bound** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Vert.** \\ **Bound** \\ **(\%)** \\ \end{tabular} } \\ \hline  & **Typical** & **RMS** & **68\% CEP** & **68\% CEP** & & \\ \hline Altitude Variation & \(5,20\) & \(32\) & \(318\) & \(1.9\) & - & - \\ \hline Rural & \(5,20\) & \(25\) & \(1637\) & N/A & \(38\) & N/A \\ \hline Suburban & \(5\) & \(75\) & \(38\) & \(1.2\) & \(72\) & \(93\) \\ \hline Urban & \(5\) & \(100\) & \(32\) & \(4.6\) & \(75\) & \(64\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the expected performance of NLP for various scenarios and environments. purposes in two-dimensional maps, it can also be used to provide altitude information as well. This altitude can be used not only for navigation, but also for barometric altitude calibrations in the absence of local pressure monitoring stations. Furthermore, these expected accuracies may assist in estimating the error bounds for NLP in various environments as a form of APNT, and a method to validate the GNSS measurements for the potential presence of spoofing [36].