Multiplicatively Regularized Source Reconstruction Method for Phaseless Planar Near-Field Antenna Measurements
Bibliographic record
Abstract
This paper approaches the source reconstruction method (SRM) for phaseless planar near-field (NF) antenna measurements by optimizing a multiplicatively regularized cost functional over the equivalent current distribution of the antenna under test (AUT). The utilized multiplicative regularization scheme, originally developed for image deblurring and inverse scattering problems, is adapted to phaseless NF antenna measurements performed over two parallel planes in front of the AUT. Current phaseless NF to far-field (FF) transformation techniques are highly dependent on the accuracy of the initial phase guess and the pattern features of the AUT. The proposed multiplicatively regularized SRM (MR-SRM) provides a robust and automated framework that can reconstruct the FF pattern of the AUT based on a nonsophisticated initial phase guess. In addition, advantages of the SRM are inherently incorporated into the MR-SRM, such as the capability for antenna diagnostics and allowing for extension to arbitrary measurement domains without the need for data interpolation. The cost functional associated with the MR-SRM is minimized with the conjugate gradient algorithm using closed-form expressions for gradient operators. The developed algorithm is presented in detail along with the synthetic and experimental examples demonstrating the method's performance in different measurement scenarios along with a comparison to alternative methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".