# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 4 Experiment
### Evaluation of Proposed Positioning Method  
The experimental environment is shown in Figure 14, which includes two rooms and a corridor adjacent to each other. Both of the rooms were 19.05 m long and 5.83 m wide, while the corridor was 19.51 m long and 1.74 m wide. Totally, 10 FTs were positioned on a tripod with a height of 1.3 m. Among them, 8 were placed in two rooms on average, where 3 were positioned in the corridor, marked with a red five-pointed star. Here, 197 testing points were set, and marked with blue squares, with an interval of 1.2 m. The sampling time of each point was 30 s and the sampling frequency was 1 Hz. Two experiments were conducted to evaluate the proposed positioning method.  
Figure 12: Recall of different ranging results.  
Figure 13: Cumulative distribution function of precisions.  
In the first experiment, the performance of the method was evaluated when a different number of FTs were set in the NLOS condition. A different number of FTs, from 0 to 7, were set outside the eastern room in turn. The ranging results and RSSIs from all FTs were collected at 67 test points in the eastern room. Then, the data were imported into MATLAB. The location was calculated using single point positioning with identification and discarding (SPP-IAD) NLOS, and that of single point positioning (SPP).  
Figure 15 shows the accuracy of the two methods. In the absence of NLOS FTs, the error of SPP-IAD is greater than that of SPP. This is because the identification model discards some ranging results from LOS FTs. However, when adding NLOS FTs, the error of SPP-IAD is smaller than SPP. As the number of NLOS FTs increases, the accuracy of SPP-IAD is basically unchanged, and the error of SPP increases. Hence, the accuracy of SPP-IAD is better than SPP when NLOS FTs are present.  
In the second experiment, the ranging results and RSSIs at all the test points were collected and the proposed method was used to carry out the positioning. Here, the maximum error (ME), error mean (EM), and root mean square error (RMSE) were used as indicators of the method.  
Figure 16 shows the EM of the SPP-IAD and SPP at each test point. Among them, 18 EMs of SPP-IAD are larger than that of the SPP, and the maximum difference is 0.3 m.  
Figure 14: Experimental area.  
Figure 15: Average positioning error with different numbers of NLOS signals.  
As shown in Table 4, the SPP-IAD was implemented with a ME of 6.862 m, an EM of 0.932 m, and an RMSE of 0.712 m, while the other has greater error than the proposed method. Compared with SPP, it achieves a ME improvement of 1.041 m (13.17%), an EM improvement of 0.337 m (36.04%), and an RMSE improvement of 0.456 m (64.04%).  
The Cumulative Distribution Functions (CDFs) of the positioning errors of SPP-IAD and SPP are shown in Figure 17. Obviously, the positioning errors of SPP-IAD are smaller than those of SPP with different y-values.  
From the two experiments, it can be concluded that the performance of SPP-IAD is better than that of SPP, both in the positioning accuracy and stability, under the condition NLOS FTs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **ME (m)** & **EM (m)** & **RMSE (m)** \\ \hline SPP-IAD & 6.862 & 0.932 & 0.712 \\ SPP & 7.903 & 1.272 & 1.268 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results of positioning errors. ME, maximum error; EM, error mean; RMSE, root mean square error.  
Figure 16: Average positioning error of each testing point.  
Figure 17: Cumulative probability distribution of positioning errors related to single point positioning with identification and discarding (SPP-IAD) and single point positioning (SPP).