# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
## 4 Experience and Analysis
### Experiments of UJIIndoorLoc Public Dataset
#### 4.1.3 Results of Position Estimation  
We chose all floors data to fully test the feasibility of DNN in position estimation and make it more effective. Tables 1-3 show the average error, root-mean-square error (RMSE), maximum error and minimum error of the DNN, as well as those of Microsoft's RADAR system. Figure 7 shows the positioning results of DNN-based Wi-Fi fingerprint location. The positioning error of RADAR is similar to that of Reference [15], while the DNN-based Wi-Fi fingerprint positioning results are better than those of the RADAR system. Compared to the RADAR system, the average errors of DNN  
Figure 5: Recognition results for different architectures. Figure 6: Effect of the dropout on recognition accuracy. on each floor were reduced as follows: (a) 7.90%, 5.20%, 9.13% and 3.50% for building 0 (Table 1); (b) 56.43%, 21.03%, 5.07% and 15.00% for building 1 (Table 2); (c) 3.66%, 15.98%, 8.64%, 27.03% and 1.32% for building 2 (Table 3). In general, the DNN-based method estimates the location of users better than the RADAR system. The localization results for different floors and different buildings show that the proposed location recognition method is highly robust in different scenes. By comparing the positioning results of the three buildings, we found that the positioning accuracy differed for each building, but the positioning results for each floor in the same building were similar. These findings mainly resulted from the deviation between the training data and the test data caused by different acquisition devices [35]. This deviation is a hot spot in the field of research on Wi-Fi signals. Many researchers have attempted to study its mechanism and solution. The relationship between the number of data acquisition devices and positioning errors is shown in Figure 8. It is apparent that the fewer devices used during the acquisition of training data, the higher the positioning accuracy. But this is not the focus of this paper, and therefore we do not carry out a detailed study. In addition, the test data were obtained four months after the original data gathering, which also contributed to increase of error. Therefore, although using the data for positioning is a challenge, DNN-based Wi-Fi \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Floors** & **Methods** & **Average Error** & **Maximum Error** & **Minimum Error** \\ \hline Floor\_0 & RADAR & 7.72 & 42.65 & 0.60 \\  & DNN & 7.11 & 42.79 & 0.22 \\ Floor\_1 & RADAR & 5.58 & 33.91 & 0.11 \\  & DNN & 5.29 & 29.87 & 0.14 \\ Floor\_2 & RADAR & 6.03 & 26.49 & 0.13 \\ Floor\_2 & DNN & 5.48 & 23.88 & 0.33 \\ Floor\_3 & RADAR & 7.14 & 31.23 & 0.08 \\  & DNN & 6.89 & 32.75 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Positioning errors of Building 0, m. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Floors** & **Methods** & **Average Error** & **Maximum Error** & **Minimum Error** \\ \hline Floor\_0 & RADAR & 19.14 & 75.99 & 0.46 \\  & DNN & 8.34 & 25.98 & 0.85 \\ Floor\_1 & RADAR & 13.17 & 54.90 & 0.19 \\  & DNN & 10.40 & 62.63 & 0.31 \\ Floor\_2 & RADAR & 13.21 & 54.91 & 0.26 \\  & DNN & 12.54 & 55.53 & 0.92 \\ Floor\_3 & RADAR & 10.27 & 58.12 & 0.37 \\  & DNN & 8.73 & 58.39 & 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Positioning errors of Building 1, m. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Floors** & **Methods** & **Average Error** & **Maximum Error** & **Minimum Error** \\ \hline Floor\_0 & RADAR & 12.57 & 77.16 & 0.94 \\  & DNN & 12.11 & 74.53 & 0.41 \\ Floor\_1 & RADAR & 9.20 & 68.21 & 0.40 \\  & DNN & 7.73 & 55.06 & 0.47 \\ Floor\_2 & RADAR & 12.97 & 65.71 & 0.48 \\  & DNN & 11.85 & 52.12 & 0.79 \\ Floor\_3 & RADAR & 11.06 & 34.42 & 9.31 \\  & DNN & 8.07 & 33.83 & 0.41 \\ Floor\_4 & RADAR & 15.88 & 74.96 & 1.39 \\  & DNN & 15.67 & 59.99 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Positioning errors of Building 2, m.  
fingerprint location recognition is more accurate than that of the widely used RADAR system, which proves the feasibility and robustness of this method.