# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
## 1 Introduction  
Unmanned aerial vehicles (UAVs) or remotely piloted aircraft systems, simply known as drones, are in high demand for in situ measurements because of their mobility, low cost, hovering capability, and low maintenance expenses. Advances in technology, such as software-defined radios (SDRs), have facilitated the utilization of UAVs for antenna and propagation measurement [1, 2]. UAVs are an effective antenna measurement solution for projects such as the square kilometer array (SKA) [3], broadcasting systems [4], largebiconical antennas [5], such as those of the French aerospace research center Office National d'Etudes et de Recherches Aerospatiales (ONERA) [6], and parabolic reflector antennas [7], because of their in situ electromagnetic field measurement capabilities. UAV measurements can also help to identify multipath-related propagation losses. In radar applications that use antenna arrays, UAV measurements are useful for the final calibration after the antennas are enclosed in radomes and deployed. This is particularly crucial in cases in which the RF system is large. Climatic conditions, such as rain, snow, or harsh weather, degrade the antenna radiation performance in terms of side-lobe level (SLL) and ripples in the beam peak. The degradation of these antenna parameters can eventually lead to a deterioration of the antenna gain and cross-polarization levels [8]. Conventional methods may not be feasible in such situations, making UAV-based measurements a viable alternative. In practice, antenna measurements with UAVs can be performed either in the Fresnel region (also known as the radiative near field) or in the Fraunhofer region (also known as the far field). For example, in shortwave communications, an antenna operating at 20-30 MHz can achieve very long communication distances owing to ionospheric propagation. Nevertheless, in this frequency range, the antenna size is large. To avoid large separation distances between the antenna under test (AUT) and the UAV, near-field measurements are an appropriate and practical alternative, whereby the near-field measurements are mathematically transformed into far-field radiation patterns. In this paper, the utilization of UAVs to perform near-field measurements is presented and involves the UAV following a specific spatial path [9; 10; 11] to collect measurements of the AUT using an SDR. Measuring antenna performance using the traditional outdoor method [12] involves measuring the antenna in the far-field region. Accordingly, these measurements require several pieces of expensive and heavy equipment. The AUT is in the receiving mode and is connected to a mixer and microwave receiver [13], which can retrieve the amplitude and phase information of the AUT. Supported by model towers, the AUT is placed on a multi-axis rotary positioner, such as an azimuth-over-elevation-over-azimuth positioner [14]. These positioners can rotate the AUT by \(360^{\circ}\) in the azimuth plane and provide limited motion in the elevation plane. Unlike traditional outdoor measurements, UAV-based measurements do not rely on large or heavy equipment. For example, instead of a heavy spectrum analyzer, an SDR, which is compact and lightweight, can be used. In addition, these SDRs can be controlled from ground level. Taking advantage of UAVs' capability to hover and perform circular trajectories would make them free from the positioners and controllers used in the traditional measurement methods. In addition to the UAV-based antenna measurements, UAVs can also be used for propagation measurements. UAVs equipped with SDRs can be used to perform propagation measurements, which involve estimating signal strength as a function of frequency and time. These measurements are then post-processed to extract the channel coefficients, such as path loss and angle of arrival, which are used to model the channel. The channel model can then be used to design an air-to-ground (ATG) communication system [15] and evaluate its performance under different operating conditions.