Calibration of the sampling jitter effect on wind direction measurement from platform-based HF radar
Bibliographic record
Abstract
A promising trend in HF systems design for ocean surface mapping application is to change it from the land-based to platform-based. One of the new challenges to be faced is the motion of the platform, which will cause sampling jitter. This paper examines the effects of random jitter in sampling locations in the radar power spectrum, from which wind direction measurements are made. Random jitter leads to, among other distortions, attenuation in power spectral estimation. This is more severe in higher frequencies. The resulting distortion of the radar power spectrum will contribute to error in the direction estimation. To help mitigate this problem, an ensemble average approach is suggested. This can lead to an accurate restoration of the power spectrum if the probability density function of the jitter is known. If the distribution is only partially known, an estimation method is proposed to reliably compute the distribution parameter of the random jitter from the distorted spectrum. Results from simulations illustrate the adverse effects of sampling jitter on the wind direction extraction and demonstrate improvements in recovery of this parameter.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".