# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
## 4 Experimental Results and Analysis
### Range-Fixed and Position-Varied Environment  
Figure 10 presents the raw Wi-Fi RTT distance measured in LOS condition in RFPV. It can be seen from the figure that there were severe fluctuations with many outliers in the measurements when the smartphone was moving and the ground truth was fixed. We can also infer that the noise here may not be Gaussian; hence, some compensation algorithms assuming Gaussian error may not apply (such as the Kalman filter). In addition, the outlier  
Figure 9: Wi-Fi RTT distance and error in RFPF and NLOS condition: **(a)** Wi-Fi RTT distance from Google Pixel 2; **(b)** Wi-Fi RTT distance from Xiaomi Redmi K20; **(c)** Wi-Fi RTT distance from LG V50S; **(d)** RMSE of Wi-Fi RTT distance from Google Pixel 2; **(e)** RMSE of Wi-Fi RTT distance from Xiaomi Redmi K20; **(f)** RMSE of Wi-Fi RTT distance from LG V50S.  
varies by phone. For example, as shown in the box plot, the outliers measured from LG, V50S are closer to the box, while the outliers from the other two phones are further.  
We then calculated the standard deviation of the measurements at each distance. As illustrated in Table 3, the standard deviations of the Wi-Fi RTT distance measured by all three smartphones from the motion are usually much higher than in static environments. It can be observed that the standard deviations of the Wi-Fi RTT distance measured by the same smartphone at different ground truth distances are similar when the phones are in the same status (either position-fixed or position-varied).  
We then added different blockers to evaluate the Wi-Fi RTT distance measurements in the NLOS condition. Similarly, we applied the offsets to the Wi-Fi RTT distance to better visualize the errors and further analysis. As shown in Figure 11, the Wi-Fi RTT distance showed the most significant error when the signal was blocked by the metal blockers and the lowest error when wooden blockers blocked the signal. As illustrated in the previous analysis, this could be caused by the skin effects that metal has better conductivity than the other two materials. We can also observe that the RMSE increased gradually as the ground truth between the AP and the smartphone went further in NLOS conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1 m** & **3 m** & **5 m** & **7 m** & **9 m** \\ \hline GP2 static & 0.115 m & 0.086 m & 0.121 m & 0.155 m & 0.056 m \\ GP2 dynamic & **0.367** m & **0.520** m & **0.708** m & **1.135** m & **0.585** m \\ XM static & 0.038 m & 0.038 m & 0.052 m & 0.051 m & 0.047 m \\ XM dynamic & **0.672** m & **0.573** m & **0.669** m & **0.620** m & **0.679** m \\ LG static & 0.191 m & 0.084 m & 0.087 m & 0.205 m & 0.059 m \\ LG dynamic & **0.514** m & **0.509** m & **0.411** m & **0.477** m & **0.375** m \\ \hline \hline \end{tabular} The bold number denotes better performance.  
\end{table}
Table 3: Comparison of standard deviation of raw Wi-Fi RTT distance in RFPF and RFPV at different range.  
Figure 10: Raw Wi-Fi RTT distance measured by the three smartphones in RFPV and LOS condition: (a) Google Pixel 2; (b) Xiaomi Redmi K20; (c) LG V50S.