Generally a UAV is mounted with multiple sensors to help in their navigation including altitude and position sensors. Although there are currently different sensors to determine the flight altitude, the acquisition of the position at every moment is a hard task to be achieved mainly in crowded scenarios where the multiple obstacles make difficult the transmission of signal coming from satellites (for global navigation systems) or base stations which do the functions of anchors in some localization systems. Another issue of this kind of scenarios is the low probability to obtain redundancy systems due to obstruction of the signal. For the case where there is only the minimum information required to estimate the position, in this work the RA-GN algorithm proposed before is applied to a localization system. In order to prove the achieved efficiency by the RA-GN algorithm as well as to analyze its accuracy, a measurement campaign was conducted in a semi-forest environment. Results reported here show that the RA-GN algorithm is able to improve the accuracy of estimation in real conditions into crowded environments, specially under critical situations where the signal is perturbed by the obstacles and even by the orientation of the tag respect to the achors compared with other positioning methods. I.