Wide-Angle Direction Finding With a Spatially Dispersive Reconfigurable Leaky Wave Antenna
Bibliographic record
Abstract
A novel radiation source reconstruction approach utilizing orthogonal interrogation modes is implemented to estimate the direction of arrival (DoA) of waves across a wide 1-D field of view (FoV) using a single-port, spatially dispersive, reconfigurable leaky wave antenna (LWA). Orthogonal interrogation modes are realized as fan-beam patterns with suppressed side lobes, steered at discrete angles within the range of$-75^{\circ } \lt \theta \lt 75^{\circ }$. A model based on full-wave simulation of the LWA is employed to optimize the capacitance values of its tunable elements, thereby forming the orthogonal interrogation modes. A prototype LWA with 30 tunable elements is fabricated, and a data-analytic procedure is developed to refine the required voltage sets for these modes. Various scenarios, including single and multiple incident waves within a wide FoV, are evaluated through both simulation and measurement. The proposed implementation successfully estimates the DoA with an error of less than 2° within the FoV of$-80^{\circ } \lt \theta \lt 80^{\circ }$. Consequently, the LWA can be integrated as a DoA estimation module at the edge of a standard reconfigurable intelligent surface (RIS), forming a self-aware wave redirection device essential for enhancing indoor and outdoor communication performance.
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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 | 0.001 | 0.001 |
| 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".