# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 3 Sub-GHz Passive Localization  
When analyzing the results, our initial focus lies with the most common use of passive fingerprinting approaches--target localization. First, we completely ignore the fact that the training measurements were performed for multiple targets and simply create a single fingerprinting database for which each location-based entry was created based on data from all six targets. Next, we investigate what happens when a fingerprint database is created based on data from only a single human individual and whether there are any decreases in accuracy when the body type of the training participant differs significantly from the evaluation participant.  
Throughout this paper, we will focus on the accuracy rate as a key metric. It is defined as the ratio of the number of correct state estimations to the total number of state estimations. For localization results, we provide error distance metrics as well, although we consider them to  
\begin{table}
\begin{tabular}{c c} \hline Environment Size & 42 m\({}^{2}\) \\ Number of Nodes & 15 \\ Protocol & DASH7 \\ Frequency Band & 433 MHz and 868 MHz \\ Transmission Power & 16 dBm \\ wtime & 10 ms \\ Occupied bandwidth/channel & 156 kHz \\ \hline \end{tabular}
\end{table}
Table 2: An overview of the most important experiment parameters. Transmission power refers to the output power as configured in the devices.  
Figure 4: Average Received Signal Strength (RSS) values with which communication between node 1 and every other node occurs, for four different locations at which a target could be present. The target is present near the upper left corner of the environment in (**a**), the upper right corner in (**b**), the lower left corner in (**c**) and the lower right corner in (**d**). All RSS values are in dBm.  
be less important due to the fact that we assume discrete states (i.e., no target was ever present outside of the 15 predefined locations).  
Additionally, we perform all of these analyses for multiple combinations of frequency bands, fingerprinting techniques and number of nodes. In regards to frequency bands, we determine separate accuracy rates using data from only the 433 MHz network, from only the 868 MHz network and from a combination of both. This combination occurs by simply appending the corresponding measurement vectors and fingerprint database entries from both networks to each other. We also determine separate accuracy rates for the two fingerprinting techniques discussed in Section 2.2--minimization of Euclidean distance and naive Bayesian classifier. Finally, because the number and exact positions of nodes can have a significant impact on localization accuracy within DFL in general [20; 21; 22], we wish to investigate this aspect as well. Given the impressive results which have been obtained in much larger environments with a very limited number of nodes within the state of the art, we strongly suspect 15 nodes to be far beyond what is required to allow for accurate localization. However, by virtually removing nodes through ignoring their RSS measurements and corresponding elements in the fingerprint database, we can obtain localization accuracies for the number of nodes varying from 2 to 15. Because each number of nodes has a large number of possible node combinations, we randomly select 100 combinations and calculate the localization accuracy for each of them. For this reason, we will primarily depict our localization results for each number of nodes as a boxplot created from the accuracy rates from these 100 selected node combinations. The only exceptions to this are 14- and 15-node scenarios, for which respectively only 15 and 1 possible combinations exist.