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Planar Near-Field Measurement for Metallic Antennas with Wide-Angle Extrapolation and Sparse Sampling Based on Dimension-Reduction of Equivalent Sources

  • Xi'an Jiaotong University

Research output: Contribution to journalArticlepeer-review

Abstract

This article presents a planar near-field measurement method for metallic antennas based on the physics-driven dimension reduction of equivalent-source unknowns, enabling both sparse sampling and wide-angle far-field radiation pattern reconstruction. First, an inverse problem is formulated to relate the planar near-field data to equivalent electric and magnetic current sources on a Huygens surface conformal to the antenna under test, which initially involves a high-dimensional unknown space. Next, by leveraging the Uniqueness Theorem, the two types of equivalent sources are reduced to a single equivalent magnetic current source, effectively halving the number of unknowns. Furthermore, the perfect electric conductor boundary condition is enforced to confine the equivalent magnetic currents to the antenna aperture, further reducing the dimensionality of the inverse problem. Simulations and measurements, including an independent anechoic chamber reference measurement, verify that the proposed method can accurately recover wide-angle far-field radiation patterns using sparse near-field sampling. Sensitivity analyses further demonstrate the robustness of the proposed method against probe positioning errors, minor model geometrical mismatches, and measurement noise. Compared with the conventional Fast Fourier Transform, Gerchberg-Papoulis, and equivalent magnetic current methods, the proposed method provides superior wide-angle extrapolation capability while significantly mitigating truncation errors and sampling redundancy.

Original languageEnglish
JournalIEEE Transactions on Instrumentation and Measurement
DOIs
StateAccepted/In press - 2026
Externally publishedYes

Keywords

  • Antenna under test (AUT)
  • dimension-reduced process
  • equivalent source
  • planar near-to-far field transformation (NFT)
  • sparse sampling

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