通过飞行校准学习无人机天线辐射图，提升通信建模精度。
Orientation Matters: Learning Radiation Patterns of Multi-Rotor UAVs In-Flight to Enhance Communication Availability Modeling
- 用飞行数据拟合球谐函数或采样加权模型来学习天线辐射图。
- 实测数据验证误差仅4.56 dB，可准确解耦双机间辐射特性。
- 适合需频繁更换载荷的无人机群自主规划与协同控制场景。
本文提出一种基于校准飞行数据的学习方法，用于获取一对异构四旋翼无人机天线辐射图（RPs）。RPs采用球谐函数级数或诱导样本加权平均进行建模。通过多项式系数的线性回归，实现对独立无人机辐射图的解耦，从而分离出联合增益中的个体贡献。在无遮挡、消声高度的同步校准轨迹下获得训练与测试样本。真实数据集评估表明，该方法能有效学习双方辐射图，外推均方根误差达4.56 dB。所提辐射图学习与解耦方法可支持载荷变更后的快速重校准，为实际应用中预期的配置变化提供精确的自主路径规划与集群控制能力。
原文摘要 · Abstract (English)
The paper presents an approach for learning antenna Radiation Patterns (RPs) of a pair of heterogeneous quadrotor Uncrewed Aerial Vehicles (UAVs) by calibration flight data. RPs are modeled either as a Spherical Harmonics series or as a weighted average over inducing samples. Linear regression of polynomial coefficients enables decoupling of independent UAVs' RPs from the observed joint gain. A synchronized calibration trajectory provides training and testing samples in an obstacle-free anechoic altitude. Evaluation on a real-world dataset demonstrates the feasibility of learning both radiation patterns, achieving 4.56 dB RMS extrapolation error. The proposed RP learning and decoupling can be exploited in rapid recalibration upon payload changes, thereby enabling precise autonomous path planning and swarm control in real-world applications where setup changes are expected.
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