Fast Antenna Testing With a Reconfigurable Near-Field Leaky-Wave Probe
Bibliographic record
Abstract
This paper presents a novel, compact, and fast antenna characterization method using a spatially dispersive, reconfigurable leaky-wave antenna (LWA) probe. In the proposed setup, the antenna under test (AUT) and the LWA are placed face-to-face in the radiative near-field region, and the voltage transmission coefficient between them is measured for various electronically controlled LWA configurations, referred to as interrogation modes. Each mode corresponds to a unique near-field plane-wave spectrum defined over a fictitious interrogation plane. An approximate transmission model is developed based on the spectral overlap of the tangential electric fields of the LWA and AUT in this plane. When the LWA’s field distributions are known a priori, the measurement process becomes a spectral-domain sensing problem. To ensure accurate reconstruction, a set of orthogonal interrogation modes with sharp main beams and low sidelobes–steered over the angular range -75∘ to 75∘–is synthesized by optimizing the LWA’s tunable varactors. The AUT’s one-dimensional tangential electric field distribution is reconstructed using Tikhonov regularization, followed by a near-field to far-field transformation to obtain its radiation pattern. The proposed approach eliminates the need for mechanical scanning in this one-dimensional configuration, significantly reducing measurement time. Simulation and experimental validation with various AUTs–including waveguides, horns, and patch arrays–demonstrate accurate far-field reconstruction, particularly for narrow-beam antennas and observation angles up to 60∘. This electronically reconfigurable setup offers a practical solution for fast, high-throughput antenna testing in modern RF and microwave systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".