Active Retrodirective Rotman Lens Antenna for Wide-Angle RCS Enhancement
Bibliographic record
Abstract
This paper presents measurement results and analysis of an innovative active retrodirective Rotman lens antenna architecture, designed to enhance the radar cross-section (RCS) for backscattering applications. Unlike conventional passive retrodirective systems, our design integrates custom-designed reflection amplifiers to significantly boost backscattered signal gain while maintaining low DC power consumption. A novel biasing technique enables independent phase and gain control of the amplifiers, ensuring a uniform array response and reducing DC power consumption by approximately 30%. Experimental monostatic RCS measurements at 5.15 GHz with a linearly polarized incident wave demonstrate a uniform RCS response over a ±40° scan angle. The integration of reflection amplifiers enhances backscattering, allowing the lens to maintain a consistent −0.5 dB RCS across the entire scan angle, which is 9 dB higher than the average RCS of a metal plate with the same effective aperture. The compact design (approximately$6\lambda \times 6\lambda $) and ultra-low power consumption (approximately 0.19 mW) make this system well-suited for low-power radar applications, such as mm-wave automotive radar sensors. These results confirm the feasibility of active-loaded phase conjugating systems for high-performance backscattering 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) | 0.001 | 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 | 0.001 | 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".