# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 5 Experimental Tests
### Discussion
Hence, it is clearly demonstrated how a properly deployed five-antenna configuration (#11), which combines three OAs and two DAs, is able to obtain almost the same performance as for configurations with 12 (#6) and 17 (#12) antennas, respectively. These observations lead us again to remark on the importance of the planning phase and how properly positioning and orienting the SR antennas, the number of required SRs can be drastically reduced to optimize the performance of the system with the minimum number of antennas. Finally, notice that the 25 th and 75 th percentiles (limits of the blue boxes in Figure 13) are really close for Configurations #6, #11 and #12. This means that the performance achieved for these configurations is stable between those values (about 82.5% and 87.5% for Configuration #11) for almost every test performed. To sum up, several conclusions can be highlighted from this study:  
* It has been observed that the monotonically-increasing tendency of accuracy (successes probability) with the number of antennas is not totally true. The planning phase (antennas' position) affects the performance of the system severely. * DAs may improve the accuracy of the system if they are properly deployed and oriented, allowing a better distinction between target areas (rooms) without excessively increasing the number of antennas. During the experiments, this is demonstrated with a configuration with five antennas (three AOs and two ADs), which results in a similar performance as the one obtained by 17 antennas (five AOs and 12 ADs). * The above point endorses that the integration of DAs increases the fingerprint quality, making RSS patterns more robust against noise and more easily recognizable by the MLP algorithm. From these remarks, it is evidenced that the addition of directional antennas enhances the performance of RSS-based systems if SRs are deployed properly. However, as mentioned previously, RSS-based systems are linked to an inherent limitation in terms of maximum accuracy due to thecomplex unpredictable variability of the RSS [16], that together with the ANN error, makes the ANN not be able to predict the room location with 100% success. At this point, it would be also interesting to compare our results with the state-of-the-art woks. To do so, we have taken into account the most recent references found in the literature related to room-level RSS-based positioning [21; 22; 23]. If we try to compare our best results in terms of accuracy and number of antennas directly, which has been for Configuration #11 _P_s (90%) = 79.4% (with five antennas), it seems we have _a priori_ inferior results compared to the over 90% average success results obtained in [21; 22; 23]. However, it is important be take into account several points to compare our results to others:  
* The metric we have employed is quite demanding, as we calculate the minimum _P_s obtained for 90% of the tests, which gives an estimation of the system reliability for 90% of the time. * The work in [21; 22; 23] provided average _P_s, which is the mean of all of the tests conducted. This metric is much poorer, as it does not talk about the reliability of the system and takes into account outliers. In fact, if we averaged the _P_s results for the 27 tests done with Configuration 11, our average success _P_s (average) = 86% (see the red line in the middle of the box for Configuration #11 in Figure 13), which seems to be much better, but it has no information about reliability. * However, another factor to take into account is the test conditions. The covered area, as the number of SR in [21; 22; 23], is quite different. For example, in [23], 17 omnidirectional antennas were employed. * Furthermore, no information regarding the conditions of the tag or the scenario is given (e.g., some questions related to the static, fixed orientation of the tag during transmission/ reception; people presence in the environment).