Development of Different Algorithms for Drone-Based Antenna Measurement Systems and Near-Field Error Analysis

Near-field antenna measurements underpin the characterization of electrically large apertures, yet the fidelity of the Near-Field to Far-Field (NF-FF) transformation depends on the reconstruction algorithm's assumptions and robustness to real-world imperfections, including those from drone-based scanning platforms. Classical FFT-based modal expansion is efficient on uniformly sampled canonical grids but fails when phase-coherent acquisition cannot be maintained. We address this via a phaseless NF-FF algorithm reconstructing the far field from amplitude-only data through iterative phase retrieval. When sampling becomes sparse or irregular, even amplitude-based methods break down, motivating the $\textbf{Adaptive Sparse Inverse Radiation Estimator (ASPIRE)}$, a full-complex inverse source framework that solves a Method-of-Moments problem over RWG basis functions via cascaded rSVD and regularized shrinkage. Mutual coupling between basis functions is explicitly resolved, improving reconstruction fidelity beyond coupling-agnostic inverse-source formulations. The solver is accelerated via a Multilevel Fast Multipole Method engine with Numba just-in-time compilation, achieving a $1.2\times$ reduction in matrix-vector product time and up to $15\times$ lower memory usage relative to dense evaluation at N=100K. Across frequency bands and positioning/truncation error scenarios, the pipeline sustains algorithmic stability and achieves sub-degree beamwidth reconstruction error. These results establish an error-aware framework for algorithm selection across fixed and drone-based near-field measurement platforms.

Drone-Based Antenna Measurement System with Optimized Positioning and ASPIRE-Based NF-FF Transformation

Unmanned Aerial Vehicle (UAV)-based antenna measurement systems provide a flexible and cost-effective alternative to conventional antenna test ranges for characterizing large and installed antennas. However, their accuracy depends on precise UAV positioning and efficient flight-time utilization, both of which are strongly influenced by the selection of drone assemblies, including the airframe, flight controller, propulsion system, positioning modules, and onboard instrumentation. This paper presents a comprehensive study of UAV-based antenna measurements with emphasis on improving positioning accuracy and optimizing flight endurance through systematic drone assembly selection. The acquired near-field measurement data are susceptible to positioning errors, amplitude and phase inconsistencies, and irregular sampling, which degrade the reconstructed far-field pattern. To address these challenges, the recorded near-field data are processed using the Adaptive Sparse Inverse Radiation Estimation (ASPIRE) algorithm. ASPIRE compensates for positioning inaccuracies and reconstructs the far-field pattern from irregularly sampled near-field data using sparse signal recovery, enabling accurate Near-Field to Far-Field (NF-FF) transformation. At 6.7125 GHz, ASPIRE achieves a residual of 1.94% and a beamwidth error of 0.4 degrees relative to a conventional facility measurement while using only 24% of the 17,298-element RWG mesh as active support. The results demonstrate that the combination of optimized drone assembly selection and ASPIRE-based NF-FF transformation significantly improves the accuracy of UAV-based antenna measurements and produces far-field patterns that closely agree with conventional antenna test range measurements.