Wind Direction Estimation Using Small-Aperture HF Radar Based on a Circular Array
Bibliographic record
Abstract
Compact high-frequency (HF) antenna arrays are convenient to deploy. However, using a small-aperture HF surface wave radar for wind direction measurement is still a challenging problem, since an unsatisfactory array pattern degrades the performance of Bragg ratio estimation. To address this issue, a digital beamforming method based on a superdirective synthesis technique for an HF receiving array that consists of seven elements positioned on a 5-m diameter circle is proposed. This superdirective beamforming method contains a sidelobe constraint. Subsequently, a hybrid superdirective beamforming and direction-finding method is adopted to estimate the wind direction using a multifrequency HF radar based on a circular array (MHF-C). The superdirective beamforming approach, as well as the wind direction estimation method, is presented in detail. The wind direction estimation method has been applied to the raw data sets that were collected with two MHF-C radars installed along the coast of the East China Sea in April 2015 and comparisons between radar-derived and in situ wind directions have been made. Ship-mounted anemometers were used to obtain in situ measurements at six sampling locations within the overlapping coverage of both radars. Another comparison between the radar-derived and anemometer-derived wind directions, which were obtained from June 15, 2015 to August 12, 2015, has also been made. The results indicate that the proposed method is effective for wind direction estimation with root-mean-square differences (RMSDs) between 24.1° and 33.1°, when wind speeds were higher than 5 m/s. The analysis encourages us to recommend a minimum wind speed of 5 m/s for reasonably assessing wind direction measurement performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".