AntennaFlow: A Generative Flow Model for Offset Correction in Phaseless Antenna Testing

Abstract: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.

Artificial Intelligence (cs.AI); Information Theory (cs.IT)

arXiv:2609.16948 [cs.AI]

(or arXiv:2609.16948v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.16948 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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