基于无人机的天线测量系统，具有优化定位和基于ASPIRE的NF-FF变换
Drone-Based Antenna Measurement System with Optimized Positioning and ASPIRE-Based NF-FF Transformation
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中文总结 AI 辅助
研究基于无人机的天线测量，通过系统选择无人机组件提高定位精度与飞行续航，用ASPIRE算法处理近场数据补偿定位不准确，实现准确的NF-FF变换，显著提升测量精度，远场模式与传统测量高度一致。
中文摘要 AI 辅助
基于无人机的天线测量系统为表征大型和已安装天线提供了灵活且经济高效的替代方案。其精度取决于无人机的精确定位和飞行时间的有效利用。本文通过系统选择无人机组件，着重提高定位精度和优化飞行续航能力。采集的近场测量数据易受定位误差等影响，导致重建远场模式退化。为此使用自适应稀疏逆辐射估计（ASPIRE）算法处理近场数据，该算法能补偿定位不准确并从不规则采样数据重建远场模式。在特定频率下，相对于传统设施测量，ASPIRE实现了1.94%的残差和0.4度的波束宽度误差，仅使用了17298个元素的RWG网格的24%作为有效支持。结果表明，优化的无人机组件选择和基于ASPIRE的NF-FF变换显著提高了基于无人机的天线测量精度，产生的远场模式与传统天线测试范围测量结果高度一致。
英文摘要
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.
