On the Synthesis of Null-Scanning Leaky-Wave Antennas (NSLWAs) for Millimeter-Wave Direction-Finding Applications
Bibliographic record
Abstract
The inborn spectral-spatial decomposition property of leaky-wave antennas (LWAs) makes them well-suitable for low-cost direction-finding (DF) applications. Conventional LWAs are characterized by a frequency-scanned directive beam, upon which the DF can be performed by searching the spectral peak of echo signals. In contrast, we study in this work a class of LWAs exhibiting frequency-scanned radiation null, i.e., null-scanning LWAs (NSLWAs), which can be exploited for DF via searching the relevant spectral null. This NSLWA consists of a pair of specially engineered LWA elements that work collaboratively to synthesize a radiation null along the scanning plane. The synthesis theories regarding how to model these LWA elements conforming to certain specifications and how to determine their excitation phases are systematically discussed. Also, a generalized design flow is summarized to facilitate practical developments of this emerging antenna class. A simple NSLWA example based on two typical microstrips combline LWA elements is constructed, simulated, and measured for case studies. Simulated and measured results are in good agreement, and both exhibit the desired characteristic of frequency-scanned radiation null. While the radiation/spectral null essentially has a larger steepness than the relevant peak, the NSLWAs may find greater potential than conventional LWAs in high-performance DF applications.
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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 | 0.000 |
| 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) | 0.001 | 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".