High-Frequency Radars: Beamforming Calibrations Using Ships as Reflectors*
Bibliographic record
Abstract
Abstract Linear array antennas and beamforming techniques offer some advantages compared to direction finding using squared arrays. The azimuthal resolution depends on the number of antenna elements and their spacing. Assuming an ideal beam pattern and no amplitude taper across the aperture, 16 antennas in a linear array spaced at half the electromagnetic wavelength theoretically provide a beam resolution of 3.5° normal to the array, and up to twice that when the beam is steered within an azimuthal range of 60° from the direction normal to the array. However, miscalibrated phases among antenna elements, cables, and receivers (e.g., caused by service activities without recalibration) can cause errors in the beam-steering direction and distortions of the beam pattern, resulting in unreliable ocean surface current and wave estimations. The present work uses opportunistic ship echoes randomly received by oceanographic high-frequency radars to correct an unusual case of severe phase differences between receiver channels, leading to a dramatic improvement of the surface current patterns. The method proposed allows for simplified calibrations of phases to account for hardware-related changes without the need to conduct the regular calibration procedure and can be applied during postprocessing of datasets acquired with insufficient calibration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".