# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 3 Sub-GHz Passive Localization
### Constructing Separate Fingerprinting Databases for Each Training Participant  
In our next step, we take a look at what happens if we only use training measurements from a single participant to construct the fingerprint database. We do so for the frequency band which has led to our most promising results thus far--a combination of 433 MHz and 868 MHz. Figure 9 shows the resulting accuracy rate box plots for both the Euclidean and naive Bayesian approaches if we use data from participant 1 to construct the database and evaluate based on data from all participants, including the training participant. It immediately becomes clear that--contrary to what hitherto had been the case--the naive Bayesian classifier actually performs worse than the Euclidean approach. In fact, the use of all 15 nodes no longer leads to a perfect accuracy rate but instead returns a rate of only 0.74 %. This could suggest that some level of overfitting occurs for the singular individual and the random stationary movements they made while the training measurements were performed. Interestingly, this leads to a situation in which the removal of a node leads to an unequivocal increase of the accuracy rate, regardless of which node is actually removed. While the results of the Euclidean approach are much more in line with what we saw earlier, the accuracy still shows a marked decline when compared to Figure 7a for which the training data from all participants was used. We look into these results more deeply and investigate the median accuracy rates for each combination of training participant and evaluation participant. We do so for both 15 nodes and 7 nodes. The 7-node case was selected because, based on the results shown in Figures 5-7, this was the least number of nodes for which a median accuracy rate above 90 % could still be obtained for both Bayesian and Euclidean approaches. Results are shown in Tables 6-9. For the 15-node Bayesian case, the use of the training data from participant 2 clearly leads to the most optimal results while the poorest accuracies are obtained with participant 1 training. In general, the highest accuracy rates for a given training participant are obtained if the same participant was also used for evaluation, but--with the exception of participant 2--these are still not equal to 1. This implies that the problem is not just a matter of overfitting to a specific participant but to a specific set of training  
Figure 9: Accuracy rates obtained for both the (**a**) Euclidean and (**b**) Bayesian methods on a combination of 433 MHz and 868 MHz RSS data, when only data from participant 1 was used for training. measurements. After all, these results in combination with Figure 7b show that the accuracy rate when locating a specific participant improves when training data from other participants is added to the fingerprint database. We will discuss this in more detail in Section 5. Similar remarks can be made for the 7-node Bayesian case. Training with participant 2 still seems to lead to the best results, with the exception of evaluating for participant 5. Additionally, while the Euclidean median accuracy rates are no longer equal to 1, they still tend to outperform their Bayesian equivalents. Interestingly, the Euclidean accuracies do not match the patterns of the Bayesian accuracies at all and participants 3 and 4 appear to be the most suitable candidates for training, rather than 2. One aspect which the results from both methods do appear to have in common, however, is the fact that in regards to evaluation, participants 1, 5 and 6 have the lowest accuracy rates. This could potentially be related to the low weight and height of participants 1 and 5, but this is not the case for participant 6. It should be noted, however, that amongst these three participants, 6 does usually have the highest accuracy rates. Further research regarding this aspect is required and we will discuss this in more detail in Section 5 as well.