Aspects of antenna pattern estimation from planar near-fields
Bibliographic record
Abstract
There are established procedures for determining the measurement uncertainty for certain pattern types (such as high or low directivity) for near-field measurement configurations [1-2-3]. This measurement uncertainly refers to the peak gain, rather than to the low directivity regions of a pattern which are seldom addressed. A very convenient configuration for pattern estimation is planar near-field sampling. The sampling density is governed by avoiding spatial aliasing of radiating waves. This paper discusses an experimental study of pattern estimation using planar near-field samples, including the effect of the sampling density on the far-fields. We use a standard professional-grade planar near-field system (NSI-200 V-5×5) to test a high-gain linearly polarized reflector antenna (10GHz 1.2m or 40 wavelength diameter) with an offset primary feed horn, and gain of about 40dB. Our account is from a typical user's viewpoint rather than from a manufacturer's viewpoint. We demonstrate that increasing the sampling density above the manufacturer's recommendation gives different far-field results for the pattern. Because the pattern is a transform of the near-field aperture, this suggests that the default sampling density of the near-field aperture is under-sampled or that the sampling is inaccurate. This highlights a grey area in the sampling requirements for the near-field region. We also demonstrate that although the accuracy of the peak gain is robust, the accuracy in low directivity regions of the main lobe is suspect.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".