UAVs play an important role in electromagnetic radiation and antenna measurements, making it possible to conduct on-site evaluations of radiation systems in a real-world environment. While the accuracy of this measurement may be somewhat reduced compared to measurements made in an anechoic chamber or at an outdoor scale, it can help evaluate the antenna radiation pattern and understand the impact of the environment on the radiation pattern.
In some cases, drones are even equipped with continuous-wave transmitters, which act as AUT receivers.
By comparing the accuracy and cost-effectiveness of UAV-based systems with traditional land-based solutions or on-site antenna characteristic measurements of manned aircraft, the results show that UAV-based systems have clear advantages.
Therefore, in order to further improve the accuracy and efficiency of the measurements, we propose a near-field measurement method that enables the reconstruction of the data without the need for additional phase sources.
This approach opens up new possibilities for surveying with improved accuracy of drone positioning and data georeferencing.
Phase-less Source Reconstruction MethodIn antenna measurements, it may not be possible to measure phase information directly using a power detector, so phase retrieval techniques are required. For phase recovery, we need to measure NF on two or more acquisition surfaces, because the spatial variation of the field distribution with distance in the AUT NF region contains enough information.
As a simplified scheme, we usually treat the infinite plane as an enclosing surface (the reconstruction domain) and establish a second equivalence principle to reconstruct the AUT radiation in the same field other than the equivalent magnetic current in the reconstruction domain.
In some cases, we can also truncate the enclosed surface to a flat surface placed on the AUT aperture for antenna diagnostics. In addition, with the NF-FF transform, we can also calculate the AUT radiation pattern.
Using the RTK system for geo-referencing with centimeter-level accuracy, as well as preliminary guessing of equivalent currents, we performed an in-depth analysis of the measured amplitude of the radiation field.
Next, we will further optimize the results by minimizing the nonlinear cost function related to the amplitude of the measurement and the amplitude of the equivalent current radiated field. We have considered nonlinear optimization techniques and described the antenna characteristics of the probe in detail.
The detection antenna is a crucial component of the UASAM project. During the design process, we looked at a number of existing options and ultimately settled on a low-directivity antenna.
The reason for this choice is that the use of directional antennas requires a probe calibration technique, but due to the uncertainty of the drone's attitude, the directivity error of the probe can have a greater impact on the noise figure (NF) measurement.
Conversely, low-polarity antennas are widely regarded as an ideal probe choice for UAV antenna measurement systems due to their stable directivity.
When choosing a probe antenna, it is important to consider not only its directivity, but also other factors such as operating frequency band, bandwidth, weight, size, and polarization purity. Printed monopole antennas meet these needs with their low directivity, lightweightness, and compact size.
The radiation pattern of these probes has been passed in 4At 65 GHz, measurements were made using the spherical range of the anechoic chamber of the University of Oviedo. Commercial monopole antennas exhibit good symmetry in the horizontal plane, while for custom hexagonal printed monopole antennas, it has better symmetry in the direction of rotation around the y-axis than commercial monopole antennas.
In subsequent tests, we used these monopole antennas as probes for 4A standard gain horn antenna of 65 GHz was benchmarked. We chose the second hexagonal printed monopole antenna as the antenna to be tested.
In the spherical range of the anechoic chamber, we measured commercial printed monopole antennas. Through these experiments, we draw useful conclusions about the selection of probes in the UAV antenna measurement system, and also provide valuable data for subsequent measurement and analysis.
We analysed the accuracy.
In UAV noise figure measurement, positioning issues are a crucial factor. Drone positioning error usually refers to the difference in distance between the target flight path and the actual flight path, while georeferencing error represents the difference in distance between the actual position of the drone and the position of the drone estimated by RTK, laser altimeter, and inertial sensors.
In free-space measurements, the distances between waypoints can be relatively large.
In the noise figure measurement, we use NF-NF technology, which can handle arbitrary geometry acquisition domains, thus solving the requirement for high-precision positioning, provided that the location of the collected data needs to be accurately georeferenced.
A recommended solution to this problem is to use a laser tracking system capable of millimeter-level accuracy at frequencies up to 40 GHz.
In the UASAM project, in order to achieve high-precision positioning and data georeferencing, we used an RTK module and a laser altimeter. While this limits the upper limit of operating frequencies to about 5-6 GHz, it still covers the operating frequency bands of a wide range of wireless communication systems, including broadcasting, mobile networks, and wireless navigation systems.
In the experiment, we used two operating frequencies of 4A 65 GHz horn antenna linear array (AUT) is placed in a spherical area inside the anechoic chamber for measurement.
By employing the equivalent current model, we have succeeded in obtaining the electromagnetic equivalent model of AUT. In order to analyze the effect of positioning error, we define two cylindrical acquisition domains with a radius of 3 meters and 4 meters and a height of 2 meters, respectively, and the coordinates of these cylindrical acquisition domains are regarded as the flight path of the target UAV.
Finally, we compare the target flight path with the actual UAV flight path, and thus obtain the quantitative results of positioning error.
After analyzing the probability density function of the error, we find that the positioning error in the x-axis and y-axis directions is about 15-30 cm, while the error in the z-axis direction is reduced to less than 10 cm.
In order to measure the georeferencing uncertainty of the RTK system, we placed the drone in a fixed position and recorded the RTK geolocation data for ten minutes. On the horizontal plane (x,y axis), the standard deviation of RTK geolocation is approximately px,y = 1-15 cm, in the height (z-axis) direction, the standard deviation increases to pz = 3-4 cm.
By introducing a laser altimeter, we have succeeded in reducing this uncertainty to pz = 1-2 cm.
In order to determine the effect of georeferencing error on the radiation pattern, we introduce a random error that conforms to the normal probability density function n(0, ), which is added to the coordinates of the flight path of the real drone.
Through this in-depth analysis, we calculated the noise figure (NF) for the following locations based on the equivalent current model of AUT: Taking into account positioning and georeferencing errors, we used the Iterative Phase Retrieval Technique (PSRM) and performed the calculation of the free space (FF) mode of AUT based on the calculated NF amplitude.
It is pointed out that even in the absence of georeferencing error, the influence of positioning error on the radiation pattern is not large, and the difference is less than 1 dB. Even taking into account the low georeferencing uncertainty of the UASAM measurements, the difference in sidelobe levels can be seen.
These differences increase further if the georeferencing uncertainty is increased to px, y = 4 cm, pz = 2 cm. By applying the iterative phase retrieval technique (PSRM), the influence of UAV positioning error on the FF mode based on NF amplitude measurement alone can be minimized.
This highlights the importance of positioning and geo-referencing errors in noise figure measurements, and provides suggestions on how to reduce their effects through advanced phase retrieval techniques, providing useful guidance for future antenna measurements and analysis.
The conclusions were verified.
To verify the performance of the UASAM antenna at high frequencies, the research team conducted S-band and C-band experiments. In the experiment, we measured the spherical range of the anechoic chamber and used this as a benchmark.
As a result, we get data for antenna diagnostics and free-space patterns. In the first experiment, we used two works in 2Horn antenna arrays in the 5 to 4 GHz band.
The signal generator provides +10 dBm of power and is delivered to the two horn antennas via a power divider. The drone is placed in the center of the airport, standing on top of a 3-meter-high pole, the ground RTK equipment is about 10 meters away from the drone, and the ground station (a laptop) is located on the side of the airport.
In our experiments, we studied a variety of acquisition areas, including planes, cylinders, and spheres. The drone can fly on a vertical axis around the AUT while maintaining a consistent orientation with that axis.
Upon further study, we found that the cylindrical acquisition path is suitable for this NF measurement, as it only introduces errors in the vertical axis. Core idea: the signal generator provides the power, which is delivered to the antenna through the power divider; The UAV is at a certain altitude, at a certain distance from ground-based RTK equipment and ground stations; The ground station is set up next to the airport; A variety of collection areas were studied in the experiment. The drone can fly on a vertical axis and maintain a consistent orientation with the axis; The cylindrical acquisition path is suitable for NF measurements in this case.
In this application example, UASAM takes approximately 15 minutes to install and set up. The measurement time for each cylinder is about 10 minutes (r=3 m) and 15 minutes (r=4.), respectively5 meters), the flight speed of the drone is 12 m sec.
Measurements are taken every 25 milliseconds, and information provided by RTKs, laser altimeters, and inertial sensors is used for geo-referenced measurements. The main and side lobes were observed in the experiment.
The results of the study clearly demonstrate the feasibility of UASAM in antenna diagnostics and characterization evaluation. The key to this research is to combine a georeferencing system with centimeter-level accuracy with an algorithm that uses only NF amplitude for measurements.
This makes it easier to perform antenna measurements in a variety of geometry acquisition domains, reducing the complexity of the hardware and sensors required for drones, enabling compact and low-power drone systems.
This approach successfully balances cost and accuracy, bringing multiple benefits to antenna measurement systems. UASAM's rapid deployment and operation makes it a great potential for rapid antenna testing in the field for a wide range of wireless communication systems.
These experimental results strongly support the practical application of UASAM and the development of future antenna measurement technology, and also emphasize the important position of the system in improving the performance and reliability of wireless communication systems.