# 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  
As explained in the preceding paragraphs, we begin with the construction of a single localization fingerprint database based on the training measurements from all experiment participants. Next, we randomly select 25 measurement vectors from each evaluation measurement. Because we performed evaluation measurements at 15 locations with six different participants, this gives us a total of \(25\times 6\times 15=2250\) measurement vectors. We apply the previously discussed Euclidean and Bayesian fingerprinting techniques to this data for all possible combinations of frequency bands and number of nodes. The resulting accuracy rates and root mean square errors for 433 MHz, 868 MHz and a combination of both are respectively shown in Figures 5-7. Several conclusions can be derived from these results regarding the impact of the number of nodes, the frequency bands and the specific classification technique that were used. First of all, the intuitive notion that lowering the number of nodes will generally cause the accuracy rate to decrease and the RMSE to increase seems to hold true. However, this is not a certainty. The precise locations of the nodes can have a significant impact on the resulting localization accuracy. To give an example, the worst combination of node positions for five nodes, on the 868 MHz frequency band and using the naive Bayesian method led to an accuracy rate of 0.53, while for the best combination this was equal to 0.94. When looking at the actual selected node combinations to obtain these specific rates as shown in Figure 8a,b, this large difference is quite evident. Figure 8a clearly shows a node selection which is concentrated on one side of the environment which leads to many potential locations which are far removed from any communication link. If a target is present in this part of the environment, the system does not have access to relevant RSS data to correctly estimate its location. Other differences can be much more difficult to explain. When selecting the nodes as shown in Figure 8c, the corresponding accuracy rate is 0.81. This is a difference of 0.13 when compared to our most optimal selection. For an actual real-life implementation of a localization system, this would be a significant decrease of the system accuracy. No immediately obvious explanation for this can be gleaned from the two-dimensional schematic overviews of the test environment as shown in Figure 8. However, we are currently looking at the effects of major positional changes, but due to small-scale fading even minor node displacements can lead to drastically different RF propagation which can therefore indirectly impact system accuracy. Figure 5: Box plots of the accuracy rates and root mean square distance errors based on 433 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. Figure 6: Box plots of the accuracy rates and root mean square distance errors based on 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. This phenomenon is well known within the field of RF-based DFL and generally a best effort approach is used when determining node locations. One highly interesting approach regarding this issue was proposed by [PERSON] et al. in [22]. In order to improve the accuracy of their RTI approach, they used servo motors which enabled them to slightly change the position of each RF sensor node in their network. Through an iterative process in which node location adjustments were made and evaluated, they managed to reduce localization errors in multiple environments by 32 % on average.