# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 3 Sub-GHz Passive Localization
### Constructing a Single Fingerprinting Database Based on All Training Data
A second conclusion is that the 433 MHz measurements seem to be capable of slightly outperforming those of the 868 MHz frequency band. This can be seen by comparing the maxima of the corresponding box plots from Figures 5 and 6. If we look at existing research within the literature, passive fingerprinting experiments performed by [PERSON] et al. [16] seemed to echo these results with the use of 433.1 MHz leading to more accurate results than with the 909.1 MHz band in a 40 m\({}^{2}\) bedroom environment. It was hypothesized that the presence of human motion caused smoother RSS variations for 433 MHz signals due to its larger wavelength and that this explained the increases in accuracy. The best results are obtained as a result of combining the two frequencies, however. The additional RSS data provided to the algorithm by combining these two sub-GHz frequencies consistently leads to more accurate localization. Using only four nodes, the naive Bayesian classifier becomes capable of obtaining a root mean square error of only 48 cm. In our earlier DFL-research, which was on the topic of Radio Tomographic Imaging, a combination of these two frequencies proved to be superior to a single-frequency system as well [23]. It should be noted that these results cannot just be generalized for all DFL systems and the optimal choice of frequency band can depend heavily on the environmental context. For example,  
Figure 7: Box plots of the accuracy rates and root mean square distance errors based on a combination of 433 MHz and 868 MHz data. (**a**) and (**b**) show the accuracy rates for respectively the Euclidean distance-based and naive Bayesian classifier-based methods, while (**c**) and (**d**) show the root mean square distance errors for both approaches. while 2.4 GHz is commonly used due to the fact that it is close to the resonance frequency of water and will therefore be strongly attenuated by the human body, it is difficult to deploy in very large environments due to its limited range. Additionally, its through-wall capabilities are limited when compared to lower frequencies and in very complex environments it might be too sensitive to multipath effects. On the other hand, sub-GHz frequencies are impacted less by human presence which can prove to be difficult for DFL as well. Finally, the results also show the superiority of the naive Bayesian classifier when compared to the Euclidean distance. This was in line with expectations, as the performance of the Euclidean distance-based approach has consistently been rather poor within the state of the art [12; 16] and it is often only used as a baseline to compare other techniques to. In our next step, we will look at our results a little more in depth and investigate whether there are any differences if we look separately at the localization accuracy for each human target. Tables 3-5 show the accuracy rates of the median node selections for all frequencies and for each of the six participants when using the naive Bayesian classifier. Figure 8: The selection of five nodes which led to accuracy rates of (**a**) 0.53; (**b**) 0.94; and (**c**) 0.81, using a naive Bayesian classifier on RSS data from the 868 MHz network. We compare the results between these three tables and look at the differences based on the frequency bands that were used. Accuracy rates for 433 MHz and 868 MHz differ only slightly from each other and significant improvements are obtained by combining the two. This seems to be the case for all evaluation participants. If we compare the median accuracy rates from the different participants, however, we do notice an interesting pattern. The worst accuracy rates are consistently obtained by Participants 1, 5 and 6, regardless of the frequency band or the number of nodes. The differences between these three participants and numbers 2, 3 and 4 are often higher than 0.10 or even 0.15. Only in 2-node cases and cases in which the accuracy rates begin to approach values above 0.90 are these