In particular, goals of this paper are (i) to determine if the wrong positioning of the drone is recognizable in real time by any manual or automated controller (i.e., the pilot or any autonomous navigation algorithm can immediately notice it); (ii) to assess the time to recover (re-fixing the phase ambiguity) as soon as a sufficient number of GNSS signals are received, depending on different operational conditions; and (iii) to assess the capability of the system to prevent the \"false-fixes\" sometimes occurring after a signal outage, delivering wrong positions under apparently good working status. To do so, an F9P RTK has been installed on a standard low-cost drone that many users own and might use for technical surveys. The output coordinates provided by the system through the well-known PX4-autopilot software, version 1.12 ([[https://px4.io/](https://px4.io/)]([https://px4.io/](https://px4.io/))), set with standard parameters, have been analyzed to assess the GNSS module's performances under various operating conditions. This paper does not focus on the best achievable RTK precision, which is reasonable at the centimeter level, but instead, it aims to cast light on the potential criticalities in kinematic positioning during a drone's flight under critical conditions, close to the surveyed object, and in almost or totally GNSS-denied environments. The issue of assessing or improving the performance of an RTK-piloted drone under GNSS-denied or critical environments has already been assessed by several authors. For instance, in [15], several sensors were implemented on a UAV to overcome denied GNSS limitations, such as IMU, pressure sensor, magnetometer, and pan-tilt-zoom camera. Also, in [16; 17; 18] visual tracking approaches were exploited to locate a drone without relying on GNSS. Drone navigation in challenging environments was tested in [19] using a heavy drone carrying a high-end inertial system. In [20], this issue was addressed by proposing a method based on the analysis of the DOP (Dilution of Precision) parameter to highlight potentially dangerous positioning performance of the GNSS, but only simulated data were used. In [21], an a priori 3D model and a point cloud of the environment were used to constrain the drone flight in conditions critical to the GNSS. Also, in [22], the navigation in a demanding urban environment was tested but using an expensive geodetic-class GNSS and coupling its data with inertial ones. In contrast, in this paper, we focus on lightweight UAVs that cannot be equipped with heavy GNSS antennas or high-end IMUs, looking at applications in unknown scenarios where such drones could be used for a first inspection. Moreover, we used real data and a real obstacle for satellite signals to test the RTK performance, exploiting experimental redundancy to spot anomalous results. The paper is organized as follows: the developed methodology to test the instruments is described in Section 2. In Section 3, the performed tests and their results are described in detail, while a discussion of the achievements is reported in Section 4. Final remarks and conclusions, as well as possible future improvements to this study, are described in Section 5.