A Joint Frequency Space Design Approach for Efficient Planar Frequency Diverse Arrays
Bibliographic record
Abstract
Abstract Antenna arrays benefit from spatial diversity, which enables the control of the pattern specifications in space. Adding frequency diversity to arrays provides an opportunity to control the beams in the Space-Time domain. Contrary to the conventional arrays, the added frequency diversity in the Frequency Diverse Arrays (FDA) leads to time-variant and range-dependent patterns. The time variation of the pattern affects both steering and auto-scanning applications. The array factor depends coherently on the frequency and spatial distributions of elements, in the same way, the spatial and time behavior of the FDA’s pattern is correlated. Due to this space-frequency coherency, an adjoint spatial-frequency design algorithm is the best approach for controlling the array's spatial and time behaviors. Although Due to the complexity of the array factor formulations in the FDA, the frequency and spatial distribution of the elements has been separately designed. This study proposes an algorithm to concurrently, allocate the location and frequency of the elements for a desired pattern. First, using some symmetry, a straightforward formulation for the array factor is obtained and used to design a symmetrical FDA for a stable and periodic scanning beam. Second, by analyzing the formulations, two important design parameters and some crucial design criteria of the FDA pattern for scanning applications are suggested, and using these parameters a designing algorithm is extracted. The novelty of the proposed approach is the simultaneous design of the location and frequency of the elements in the space-frequency plane, which results in meeting the time and spatial requirement of the pattern. Using this approach, two different planar arrays are designed, and their results are compared with those of other planar configurations. This study paves the way for a new approach to designing FDAs.
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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 | 0.001 |
| 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) | 0.002 | 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".