Wide-Angle Impedance Matching of a Patch Antenna Phased Array Using Artificial Dielectric Sheets
Bibliographic record
Abstract
One main challenge with wide-angle scanning phased arrays is the substantial variation of the active reflection coefficient with scan angle. In this paper, the use of artificial dielectric sheets is proposed for reducing the spread of the active reflection coefficient with scan angle. The design method is illustrated based on the impedance matching performed in front of the array face using the admittance seen looking into the array from space. A novel metasurface is developed based on subwavelength-sized conductive particles patterned on both sides of a thin dielectric sheet. Loading a thin dielectric sheet with these particles enables the effective susceptance of the sheet to be tuned. The variation of the array reflection coefficient with scan angle is reduced by providing the required susceptance at a certain distance in front of the array face using the sheet. Two different particle shapes are used to load the dielectric sheet, namely, strips and split rings. It is shown that the proposed artificial dielectric sheet is capable of improving the active reflection coefficient when the mutual coupling is the primary factor in limiting the scan range. Moreover, it is shown that the proposed design is suitable for phased arrays with a wide frequency bandwidth. Finally, simulation and measurement results for a uniform 9×9 element patch antenna array, compensated using this method, are presented. The finite compensated array has an active VSWR less than 2 for approximately a ±45° scan range in the E plane, a ±65° scan range in the H plane, and a ±80° scan range in the D plane.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".