# Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
## 6 Performance Evaluation
### Analysis of Positioning Accuracy  
To analyze the effect of our proposed ranging technique on the range-based localization accuracy, we used the ILS positioning method discussed in Section 3. In particular, to reflect a change in the channel environment, we assumed a change in the AP deployment, as shown in Figure 12. The fingerprint-based methods cannot be applied without a new site survey, and the robustness of the proposed hybrid localization can be verified in this scenario.  
Figure 13a shows the ECDF of the root mean square error (RMSE) between the estimated and actual positions for a total of 1641 points. The key indicators are summarized in Table 2. In terms of statistical results, the performance of the proposed method was superior to that of the existing methods. The average RMSE of the proposed method is similar to that of Benchmark 1 due to the outlier result of the ILS positioning method. When comparing the median RMSEs, our proposed positioning method showed a lower error than those of other existing methods by at least 22.3%. Positioning results using the single-band RSS of 2.4 or 5.2 GHz had median errors of 2.73 m and 3.03 m, respectively, even when the deep regression method was applied. These are 3.8% and 9% smaller errors, compared to the results of using 2-slope PLM-based method for each band. Meanwhile, the result of benchmarks 3, which was the result of the existing rule-based method using the dual-band RSS, was 2.56 m. Additionally, the deep regression ranging method using the dual-band RSS could reduce the median of positioning error to 1.99 m. It was a 22.3% improved result compared to the result of benchmark 3. The effect of the deep regression method was more significant with the dual-band RSS than with the single-band RSS.  
Figure 11: Average ranging error according to the structure of the neural network.  
Our proposed method is more effective for errors of the 50 th percentile or less. The histogram in Figure 13b shows the distribution of spatial positioning errors. In each histogram, the x-axis represents positioning RMSE, and the y-axis represents count. The result shows that proposed method improved the overall positioning accuracy, and increases the number of cases estimated within 2 m. This is because the proposed algorithm effectively reduces the ranging error when the AP and the target device are actually close to each other, but are blocked by walls.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ranging Method**} & \multicolumn{4}{c}{**Positioning Error [m]**} \\ \cline{2-5}  & **25\%-tile** & **50\%-tile** & **90\%-tile** & **Average** \\ \hline Benchmark1 & 1.85 & 2.84 & 6.09 & 3.82 \\ Benchmark2 & 2.11 & 3.33 & 6.63 & 4.98 \\ Benchmark3 & 1.66 & 2.56 & 5.69 & 11.38 \\ Benchmark4 & 1.67 & 2.73 & 5.70 & 28.38 \\ Benchmark5 & 1.93 & 3.03 & 6.33 & 4.82 \\ Proposed method & 1.14 & 1.99 & 5.19 & 3.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Positioning error results for each ranging method.  
Figure 12: A top view of the test environment.  
Figure 13: Empirical cumulative distribution function (**a**) and histogram (**b**) of the positioning error.