Enhancing multiple-aperture Ka-band Navy SATCOM antennas with electronic tracking and reflectarrays
Bibliographic record
Abstract
In naval SATCOM applications, there are advantages to using multiple antenna panels, each of limited scan range covering only a portion of the required hemisphere. For mechanical steering, the depth of each panel can be reduced by dividing the required aperture into multiple, smaller, sub-apertures at the cost of needing coherent combining. Two enhancements to multiple sub-aperture systems are presented. First, a method, akin to radar monopulse tracking techniques, is introduced that uses the multiple sub-beams for fine acquisition and tracking of the downlink signal. The individual sub-beams are steered to point slightly off center. A portion of the downlink signal from each beam is tapped off, detected, and used in an algorithm to compute the pointing error of the combined antenna beam center. This error estimate is used in a feedback loop to track the downlink signal. In the second enhancement, the sub-aperture antennas are implemented using reflectarray reflectors. Printed reflectarrays are used in place of conventional parabolic dishes. To minimize the design complexity of the feeds, the reflectarray elements are designed to transform the linear polarization from the feeds to circular polarization. The reflectarray design is capable of handling both right-hand and left-hand circular polarization. Furthermore, the reflectarray is designed with separate focal points for the transmit and receive signals. This removes the need for an orthomode transducer and for diplexers, resulting in considerable cost and weight savings. A Ka-band implementation is described.
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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) | 0.003 | 0.003 |
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".