# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 4 Sub-GHz Passive Identification
A potential explanation for this result could be the small weight and height of participant 1 as provided in Table 1. The differences in body type compared to the other participants could lead to such vastly different impacts on the RSS measurements that it becomes very easy to differentiate them. Indeed, whether or not differences in body type can lead to differentiation between individuals within \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Estimate**} \\ \hline  & **Participant \#** & **1** & **2** & **3** & **4** \\ \hline  & 1 & 0.99 & 0 & 0 & 0.01 \\ \cline{2-6} Ground Truth & 2 & 0 & 0.15 & 0.57 & 0.29 \\ \cline{2-6} Ground Truth & 3 & 0 & 0 & 0.42 & 0.58 \\ \cline{2-6} Ground Truth & 4 & 0 & 0.02 & 0.47 & 0.51 \\ \cline{2-6}  & 5 & 0.15 & 0.08 & 0.18 & 0.60 \\ \cline{2-6}  & 6 & 0.07 & 0.01 & 0.22 & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Confusion matrix for identification based on an Euclidean distance minimization approach applied to RSS data from the 868 MHz network. Figure 10: Identification accuracy rates for each location for both the (**a**) Euclidean distance minimization and (**b**) naive Bayesian classifier approach applied to 433 MHz RSS data. an RSS-based passive fingerprinting system is one of the main questions driving this research. Now that we can answer this question in the affirmative based on this result, the next step consists of investigating how large differences in body type need to be in order to enable this differentiation. The only previously existing RSS-based passive identification study is--to the best of our knowledge--by [PERSON] et al. [18] and made use of three whose body types were vastly different (a young child, a regularly sized woman and a tall man) and they only managed to obtain a total accuracy rate of 0.67. Additionally, it should be noted that our confusion matrices indicate that whenever data from participant 5 was used, it was only very rarely estimated to be participant 1, despite the fact that their weight and height were closest when compared to all the other participants. While the very accuracy with which participant 1 can be differentiated from the other individuals is an encouraging step in the right direction, we do believe that there is much more research that needs to be performed and we will discuss this in the next section. \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 & 0.99 & 0.01 & 0 & 0 \\ \cline{2-6}  & 2 & 0 & 0.28 & 0.63 & 0.10 \\ \cline{2-6}  & 3 & 0 & 0 & 0.21 & 0.79 \\ \cline{2-6}  & 4 & 0 & 0 & 0.15 & 0.85 \\ \cline{2-6}  & 5 & 0.03 & 0.16 & 0.12 & 0.69 \\ \cline{2-6}  & 6 & 0 & 0.19 & 0.12 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Confusion matrix for identification based on a naive Bayesian classifier approach applied to RSS data from the 868 MHz network. \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 & 0.99 & 0.01 & 0 & 0 \\ \cline{2-6}  & 2 & 0 & 0.28 & 0.63 & 0.10 \\ \cline{2-6}  & 3 & 0 & 0 & 0.21 & 0.79 \\ \cline{2-6}  & 4 & 0 & 0 & 0.15 & 0.85 \\ \cline{2-6}  & 5 & 0.03 & 0.16 & 0.12 & 0.69 \\ \cline{2-6}  & 6 & 0 & 0.19 & 0.12 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Confusion matrix for identification based on an Euclidean distance minimization approach applied to RSS data from both the 433 MHz and 868 MHz network.