## 3 Results and Discussion
### RSSI Localization
#### 3.2.2 RSSI Localization  
The results of the localization test are presented in Figure 10 which illustrates how the localization error changes with the user orientation. Anchors A, B, and C correspond to routers A, B, and C, respectively, in Figure 4. The bar values are averages of the sampled results, and each bar includes the standard deviation of the measurements. The red bars represent the localization error calculated by the trilateration algorithm, and blue bars represent the min-max algorithm. Figure 10 shows that the min-max algorithm yields better results and lower localization error than the classic trilateration algorithm. The localization error for the min-max algorithm ranges from 4 m in the best case to 7 m in the worst case. The lowest localization error from  
Figure 10: Localization error. the trilateration algorithm is 4.5 m, and the highest is 10 m. The relatively good performance achieved by the min-max algorithm is mainly due to its tendency to locate the user in the center of the area defined by vertical and horizontal lines, as described in Section 2.2.2, using the wireless network for localization. The lowest localization error and lowest standard deviation were obtained when the user faced router B. This can be explained by examining the testbed geometry in Figure 4. Because the user was facing router B, he had a direct line of sight to it. Moreover, the user had a partial line of sight to router A as well. This finding is due to the position of router B, which is lower on the y-axis than router A. The low localization error in this test case can be attributed to the existence of these lines of sight, which was not the case in the other two tests. If we refer again to the geometry in Figure 4, it can be expected that when the user faces router C, the localization error will be highest because the other two routers (A and B) are behind the user. In conclusion, a high localization error is a consequence of an unpredictable radio channel and a relatively small number of anchors. As observed in [10], the localization error decreases as the number of anchors increases. The behavior of the three approaches for UPD localization, namely, internal GNSS, external DGNSS, and RSSI, has been analyzed using real data from the RHEA scenario. We have shown that the external DGNSS approach yields better performance than the other approaches. However, external DGNSS is also the most expensive approach and requires the operator to carry external equipment. Moreover, the internal GNSS solution has two significant advantages over the other two approaches: (1) it also offers sub-meter precision and (2) the positioning module is integrated into the portable device and can thus be easily used. Although RSSI localization algorithms are primarily useful for indoor positioning and their positioning accuracy is not comparable to the accuracy provided by GNSS, we decided to test them in a dynamic outdoor environment as a complementary solution to the existing GNSS modules. However, the results showed that RSSI localization produces a substantial positioning error, and thus, the proposed localization technique has substantial limitations when used in the RHEA scenario, in which humans and autonomous vehicles share the same space. [PERSON] _et al._[18] measured the GNSS localization error in various agricultural environments: an open field, a small forest, and a medium forest. Interestingly, the average localization error in the open field was 1 m, 8 m in a small forest, and nearly 10 m in medium forest. These results encourage the use of a hybrid solution in forests because the achieved localization error is, in some cases, smaller than that reported in [18]. However, two assumptions must be tested for the hybrid method to outperform traditional GNSS localization in forests: (1) anchor nodes, _i.e._, autonomous tractors, must be able to acquire GNSS signals due to high-end equipment (RTK) and (2) the existing channel model must be adapted to the new environment.