THE MULTIPLE ANTENNA INDUCED EMF METHOD FOR THE PRECISE CALCULATION OF THE COUPLING MATRIX IN A RECEIVING ANTENNA ARRAY
Bibliographic record
Abstract
Practical antenna array designs generally require that the elements are separated by electrically short distances. The resultant mutual coupling often adversely afiects the achievable performance. Various methods are available to quantify the efiects of mutual coupling in arrays and improve performance through mutual coupling compensation. Mutual coupling is often described by a coupling matrix that relates the coupled and uncoupled quantities. Unfortunately, the accuracy with which the coupling matrix can be calculated is highly dependent on both the method selected and the frequency. This is a signiflcant limitation for wideband analysis where the coupling matrix needs to be calculated accurately at all frequencies of interest. This paper introduces a novel method for the precise calculation of the coupling matrix at any frequency of interest. It is an extension of the induced EMF method to multiple array elements. The method has the important practical advantage of being independent of the numerical technique used in the analysis. Since the coupling matrix is calculated by exciting the elements in the transmission mode, the method resembles well-known network analysis. However, as outlined in the paper, there are subtle difierences between the two approaches, which lead to more accurate results with the new proposed method. It is also demonstrated that antennas with arbitrary geometries and illuminations are handled accurately by the method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".