基于无人机的天线测量系统及近场误差分析的不同算法开发
Development of Different Algorithms for Drone-Based Antenna Measurement Systems and Near-Field Error Analysis
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中文总结 AI 辅助
针对无人机近场天线测量的NF-FF变换保真度问题，提出ASPIRE算法，结合迭代相位检索与级联rSVD等技术，实现亚度级波束宽度重建误差，建立误差感知的算法选择框架。
中文摘要 AI 辅助
近场天线测量是电大型口径天线特性表征的基础，但近场-远场（NF-FF）变换的保真度取决于重建算法的假设以及对无人机扫描平台等现实缺陷的鲁棒性。经典基于FFT的模态展开在均匀采样的标准网格上效率较高，但在无法保持相位相干采集时失效。为此，我们提出一种无相位NF-FF算法，通过迭代相位检索仅从幅度数据重建远场。当采样变得稀疏或不规则时，即使基于幅度的方法也会失效，这促使我们开发了**自适应稀疏逆辐射估计器（Adaptive Sparse Inverse Radiation Estimator, ASPIRE）**，这是一种全复逆源框架，通过级联rSVD和正则化收缩求解基于RWG基函数的矩量法问题。该算法明确解决了基函数间的互耦合问题，相较于不考虑耦合的逆源公式，提升了重建保真度。求解器通过带有Numba即时编译的多层快速多极子方法引擎加速，在N=100K时，相较于稠密评估，矩阵-向量乘积时间减少1.2倍，内存使用量降低多达15倍。在不同频段以及定位/截断误差场景下，该流程保持算法稳定性，实现亚度级波束宽度重建误差。这些结果为固定和基于无人机的近场测量平台建立了一种感知误差的算法选择框架。
英文摘要
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.
