Impact of Element Pattern Symmetry on the Effective Degrees of Freedom in Dual-Polarized GNSS Adaptive Antenna Arrays
Bibliographic record
Abstract
The effect of element pattern symmetry on the performance of dual-polarized global navigation satellite system adaptive antenna arrays is investigated. To this end, using analytical expressions for the far-fields of circular microstrip antennas, the steady-state beamforming performance of a two-element dual-polarized array with three degrees of freedom (DOFs) is studied through many Monte Carlo simulations. It is shown that in the presence of randomly polarized interferers, regardless of the element pattern symmetry, the dual-polarized array can use all its DOF effectively to suppress up to three interferers and is as effective as a four-element right-handed circularly polarized (RHCP) array. It is, however, shown that if the interferers are RHCP, the dual-polarized array can use its DOF effectively only when the element patterns are asymmetric (unequal in E- and H-planes) and is ineffective in suppressing more than one interferer when the element patterns are symmetric (equal in E- and H-planes). This limitation is important in practice if the interferers are known to be RHCP and the array ground plane is small, since the patterns of microstrip antennas with small ground planes can become nearly symmetric. This finding is verified with a fabricated prototype of a two-element dual-polarized array with a small ground 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".