## 4 Experimental Environment and Results
### Experimental Results
#### 4.2.4 Performance Evaluation  
We evaluate the performance of our proposed ranging method under the two representative indoor environments. Figure 18 presents the Cumulative Distribution Function (CDF) of distance errors with the four ranging methods in the corridor experiment. In the IT-1 building experiment, the mean distance error is about 1.049 m and a standard deviation of 0.623 m for our proposed ranging method. In Figure 19, we plot the CDF of distance errors with the four ranging methods in the IT-2 building of KNU. In this propagation environment, our proposed method can achieve a 1.5 m distance error for over 70% of the reference points. The algorithm we proposed can obtain the best ranging results, and is the most accurate of the four ranging methods.  
Figure 17: The comparison of the estimated distance error value between 1 m and 10 m.  
Figure 18: CDF of distance errors with the four ranging algorithms in IT-1 building.  
Figures 18 and 19 shows our proposed ranging method outperforms the existing ranging methods in both scenarios. The RSSI-based ranging method and the CSI-based ranging methods do not perform as well as our RSSI and CSI-based scheme. In the IT-1 building environment, the RSSI-based ranging method only manages to achieve 25% of the points that have an error under 1.5 m. The filtered RSSI-based ranging method only manages to achieve 36% of the points that have an error under 1.5 m. This is in contrast to that of the CSI-based ranging method which achieves 67% of the points that have an error under 1.5 m. These same methods achieved 30%, 37% and 60% of the points that have an error under 1.5 m in IT-2 building environment respectively.  
We evaluate the performance of the proposed ranging algorithm based on RSSI and CSI using EKF through the statistical results of two indoor environments. Tables 3 and 4 list the average and standard deviation of the ranging error, respectively. Unlike FILA, the proposed algorithm utilizes RSSI and various CSI subcarriers acquired on each antenna. The algorithm can achieve higher accuracy. Two ranging methods using CSI (i.e., the proposed method and FILA) are superior to the two ranging methods based on RSSI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indoor Ranging Algorithm** & **Mean Ranging Error** & **Standard Deviation** \\ \hline RSSI-based algorithm & 1.798 m & 1.279 m \\ Filtered RSSI-based algorithm & 1.669 m & 1.243 m \\ CSI-based algorithm (FILA) & 1.395 m & 0.972 m \\ RSSI and CSI-based algorithm (Proposed algorithm) & 1.103 m & 0.612 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of four indoor ranging algorithms (IT-2 building).  
Figure 19: Cumulative Distribution Function (CDF) of distance errors with the four ranging algorithms in IT-2 building.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indoor Ranging Algorithm** & **Mean Ranging Error** & **Standard Deviation** \\ \hline RSSI-based algorithm & 2.041 m & 1.214 m \\ Filtered RSSI-based algorithm & 1.696 m & 0.908 m \\ CSI-based algorithm (FILA) & 1.381 m & 0.577 m \\ RSSI and CSI-based algorithm (Proposed algorithm) & 1.049 m & 0.623 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of four indoor ranging algorithms (IT-1 building).  
Different from the RSSI, the filtered RSSI and the CSI-based ranging methods, our RSSI and CSI-based ranging method using EKF suppresses rich multipath effects in an indoor environment. It also achieves significantly higher accuracy. This result indicates that our proposed method is able to effectively estimate and compensate for the ranging result.