On the Synthesis of Full-Spectrum-Accessible Leaky-Wave Array Systems for Wideband Frequency-Scanning Radar Applications
Bibliographic record
Abstract
For traditional frequency-scanning radars (FSRs), the range resolution is unavoidably worse than its expected value due to the inefficient use of the given signal spectrum. This issue, which intrinsically renders FSRs less competitive than typical frequency-modulated continuous-wave radars (FMCWRs), can be well tackled by employing the filter-bank leaky-wave antenna (FB-LWA) array system (as proposed in our previous work) to enable FSRs with full-spectrum-accessible capabilities. While the theoretical mapping relation between the FSR's characteristic system parameters and the FB-LWA array's design information is currently uncharted in communities (by contrast, a similar mapping relation is well known in FMCWRs), our work aims to fill such a gap by establishing a relevant synthesis theory for the FB-LWA array system. Notably, two critical issues related to the array synthesis are carefully addressed, namely (i) how FSRs can achieve the same range resolution as FMCWRs for a given signal bandwidth, and (ii) when FSRs can use fewer channels while sharing the same range resolution as FMCWRs. Additionally, to facilitate the synthesis of an FB-LWA array in practice, a streamlined three-step design flow is summarized, which is then followed by the modeling, simulation, and measurement of a four-element microstrip combline FB-LWA array for case studies and demonstration. It is shown that the synthesis theory presented in this work makes it straightforward to build an FB-LWA array once radar system parameters are determined, paving the way for the future deployment of full-spectrum-accessible FSRs.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".