AntennaFlow：无相位天线测试中偏移校正的生成流模型
AntennaFlow: A Generative Flow Model for Offset Correction in Phaseless Antenna Testing
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- Nanyang Technological University（南洋理工大学）
- Beihang University（北京航空航天大学）
- University of Electronic Science and Technology of China（电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
AntennaFlow提出三阶段生成流模型，联合解决无相位天线测试中相位缺失与偏移安装问题，实现从稀疏幅度测量快速重建近远场，优于现有方法。
中文摘要 AI 辅助
近场到远场变换是大口径天线测试的核心，然而仍存在两个相互关联的挑战：毫米波频段下昂贵的相位采集，以及偏移安装下中心假设的违背。现有方法分别处理这些问题，要么需要密集的全场数据，要么需要偏移向量。我们利用一个关键观察来联合解决这两个问题：不同偏移下的幅度场是同一近场的坐标变换视图。挑战在于在没有相位或偏移向量的情况下，从偏移幅度中恢复中心对齐的场。我们提出AntennaFlow，一个三阶段框架：一个对比学习编码器，将偏移视图映射到偏移不变的嵌入；一个确定性流匹配传输，将偏移幅度映射到中心对齐的幅度；以及简化外推技术，其格林函数泰勒展开仅对中心场有效。实验表明，AntennaFlow能够从稀疏的仅幅度测量中实现快速、无相位、无偏移向量的近场到远场重建，在保持物理一致性的同时，始终优于现有基线。
英文摘要
Near-field to far-field transformation is central to large-aperture antenna testing, yet two coupled challenges remain: costly phase acquisition at millimeter-wave bands and violations of the centering assumption under offset mounting. Existing methods address these issues separately, requiring either dense full-field data or offset vectors. We tackle both jointly by exploiting a key observation: amplitude fields under different offsets are coordinate-transformed views of the same near field. The challenge is to recover the center-aligned field from offset amplitudes without a phase or offset vector. We propose AntennaFlow, a three-stage framework: a contrastively learned encoder that maps offset views to an offset-invariant embedding, a deterministic flow-matching transport that maps offset amplitudes to center-aligned ones, and the Simplified Extrapolation Technique, whose Green-function Taylor expansion is valid only for centered fields. Experiments show that AntennaFlow enables fast, phaseless, offset-vector-free NF--FF reconstruction from sparse amplitude-only measurements, consistently outperforming existing baselines while preserving physical consistency.
