A Direction-Finding Model With Spatial Polarization Characteristics
Bibliographic record
Abstract
The representation of electromagnetic wave polarizations and co-polarizations of antenna elements has extensively been studied and documented. Various approaches to the description of polarization have been derived across different applications, and these different definitions may lead to confusion and ambiguity. In addition, in a joint direction of arrival (DoA) and polarization estimation, the receiving antenna polarization in the maximum gain direction is usually equated to the one from any incoming wave direction, leading to unacceptable estimation errors. To this end, a direction-finding model is proposed in this article, which makes use of arbitrarily polarized antennas to explore spatial polarization characteristics. Specifically, the relationship between the co-polarization of a transmitting and receiving antenna and the polarization of radiated electromagnetic waves is derived from a unified Cartesian coordinate system. Subsequently, a definition of quantification for antenna polarization is proposed, which can represent the polarization in direction-finding solutions more naturally and intuitively. In consideration of rotation and spatial polarization characteristics of the receiving antenna, the proposed direction-finding model is thus developed with a unified polarization representation. Moreover, an application algorithm based on this model is formulated. The attractive features of the proposed model are verified by simulations and experiments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".