# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 4 Sub-GHz Passive Identification  
In the previous section we have observed that the choice of participant(s) for both training and evaluation could have a significant impact on the localization accuracy. We will now investigate whether the different impacts of different participants can be used to distinguish between them. Instead of creating a single fingerprinting database with different entries for different locations, we create a separate database for each location with each entry corresponding to one of the four training participants. We assume that the location of the target is known, as the previous section has shown how trivial it is to obtain an accuracy of 100 % with 15 nodes if training data from multiple individuals is used. We randomly select 25 measurement vectors from each evaluation participant dataset and then construct confusion matrices for each location. Finally, we combine these confusion matrices into one large matrix representing the identification accuracy for the entire environment. Additionally, we present the accuracy rates for each location. Due to the fact that no training measurements exist for them, results for participants 5 and 6--while still shown in the confusion matrices--are not taken into account when calculating these accuracy rates. All of the steps described in the preceding paragraph are performed for 433 MHz, 868 MHz and the two frequency bands combined, using both the Bayesian and Euclidean methods. Table 10 shows the total confusion matrix for the Euclidean method used on 433 MHz data and in Table 11 the same results can be found for the Bayesian approach. For both methods, the total confusion matrix was created by the summation of the separate confusion matrices for each location. Based on these confusion matrices, the accuracy rates for each location were calculated. These are shown in Figure 9(a),b. Tables 12 and 13 and the corresponding Figure 9(a),b show the same results for 868 MHz data. Finally, Tables 14 and 15 and Figure 11(a),b show this for a combination of both frequencies as well. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Estimate**} \\ \hline  & **Participant \#** & **1** & **2** & **3** & **4** \\ \hline \multirow{5}{*}{Ground Truth} & 1 & 1 & 0 & 0 & 0 \\ \cline{2-6}  & 2 & 0 & 0.07 & 0.78 & 0.15 \\ \cline{2-6}  & 3 & 0 & 0.01 & 0.13 & 0.86 \\ \cline{2-6}  & 4 & 0 & 0.06 & 0.26 & 0.68 \\ \cline{2-6}  & 5 & 0.01 & 0 & 0.02 & 0.98 \\ \cline{2-6}  & 6 & 0 & 0.02 & 0.59 & 0.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Confusion matrix for identification based on an Euclidean distance minimization approach applied to RSS data from the 433 MHz network. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Evaluation**} \\ \hline  & **Participant \#** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{5}{*}{Training} & 1 & 0.98 & 0.70 & 0.76 & 0.73 & 0.61 & 0.70 \\ \cline{2-6}  & 2 & 0.73 & 0.99 & 0.97 & 0.96 & 0.73 & 0.91 \\ \cline{2-6}  & 3 & 0.69 & 0.93 & 0.93 & 0.91 & 0.73 & 0.86 \\ \cline{2-6}  & 4 & 0.62 & 0.89 & 0.93 & 0.93 & 0.83 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Median accuracy rates for each combination of training and evaluation participants. Results were obtained through the use of a naive Bayesian classifier on RSS data from a 7-node 433 and 868 MHz network. We can derive many conclusions from these results. First of all, the identification results for most participants tend to be very poor, with the important exception of participant 1. Depending on the selected frequency and fingerprinting approach, the rate with which this participant is correctly identified varies between 0.93 and 1. This stands in stark contrast to the other participants, whose accuracy rates vary respectively between 0.10 and 0.62, 0.13 and 0.42 and 0.48 and 0.85 for participants 2, 3 and 4.