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.