Frequency diverse array with random logarithmically increasing frequency offset
Bibliographic record
Abstract
Abstract The frequency diverse arrays (FDAs) have been extensively investigated since it can provide a unique angle‐range‐dependent beampattern. This range‐dependent characteristic can suppress the range ambiguous clutters, leading to many novel potential Radar applications. Two issues, however, exist in conventional FDA (CFDA) beampatterns, that is, range‐angle coupling and range periodicity. Therefore, for a CFDA Radar, target range information cannot be obtained directly from the beampattern peaks and target ambiguity occurs in the range domain. In this work, a new frequency diverse array (FDA) transmitter architecture with random logarithmically increasing frequency offset (log‐RFDA) is presented. It is shown that the proposed strategy provides a nonperiodic thumbtack‐like beampattern, eliminating the ambiguity in range domain, suffered in the CFDA Radar. Furthermore, reduced side lobe and higher detection resolution can be achieved, compared with the previously reported log‐FDA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".