Broadband Multiple Cone-Beam 3-D IIR Digital Filters Applied to Planar Dense Aperture Arrays
Bibliographic record
Abstract
A digital beamformer that can synthesize array factors (AFs) with multiple, ultrawideband (UWB), frequency-independent beams at lower computational complexity is proposed. The beamformer is based on a novel 3-D infinite impulse response (IIR) transfer function$H_{\rm MC}\left({\bf z}\right)$having multiple cone-shaped passbands in the 3-D spatio–temporal (ST) frequency-domain$\mmb{\omega}\in\BBR^{3}$. The magnitude frequency response and the AF of$H_{\rm MC}\left({\bf z}\right)$are simulated for dual- and single-passband cases. An element pattern of a broadband Vivaldi antenna is simulated at 1.4 GHz and is used to obtain the total array pattern. Computational complexity of$H_{\rm MC}\left({\bf z}\right)$for single-passband (1Cone) case and that of the conventional phased array (PA) beamformer are derived. The magnitude frequency response of the proposed beamformer for 1Cone case and that of the PA beamformer are compared using the mean square error (MSE). For the given selectivity specified by the half cone angle$\epsilon={\hbox {5}}^{\circ}$, proposed beamformer provides around 60% lower MSE for the same complexity and around 90% lower complexity for the same MSE compared with the PA beamformer.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".