Seed Prediction for Phase Retrieval Algorithms for Scanned Aperture Antennas Using Deep Learning
Bibliographic record
Abstract
Phase retrieval algorithms are known to be sensitive to the seed, i.e., the initial phase approximation at the antenna aperture. An example where this seed can be critical is while considering an aperture antenna that scans a beam. This paper utilizes deep learning to provide a suitable seed to efficiently guide the phase retrieval algorithm for quick convergence on the accurate phase distribution thereby enabling near-field phaseless measurements for scanned aperture antennas. To create data sets for training and evaluations, an infinitesimal dipole array is employed. In this paper, we train an ANN model with three hidden layers on a representative circular array with various scan extents in elevation and azimuth, and then validate/test this trained model on arrays with different aperture sizes and geometries. The evaluations illustrate that the trained model can provide an accurate prediction of the scan angle over multiple antenna geometries especially when the aperture size of the antenna evaluated is lesser than the aperture size of the antenna used for training.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".