The Effect of Radar Ocean Surface Sampling on Wave Spectrum Estimation Using X-Band Marine Radar
Bibliographic record
Abstract
In this paper, the effect of the ocean surface sampling process on the ocean wave spectral estimation using the Cartesian Fourier transform (CFT) method on X-band marine radar data is investigated. Our analysis shows that the ocean surface sampling process involves a spatial averaging process that might be described as a 2-D low pass filter. Furthermore, a filter referred to as the inverse sampling averaging filter (ISAF) is proposed to be integrated with the CFT method in order to mitigate the effect of the sampling process. For validation, the CFT-with-ISAF method as well as the CFT-without-ISAF method were used to estimate ocean wave spectra and sea state parameters from X-band marine radar field data. The estimates from both methods were compared to ground truth estimates generated using TRIAXYS wave buoy data. The results show that the ISAF improves the CFT method in estimating ocean wave spectra. The recorded accuracy improvements in estimating the non-directional wave spectrum, the peak wave period, the mean wave period, the zero-crossing wave period, and the peak wave direction were 11%, 12%, 21%, 17%, and 34%, respectively. The performances of significant wave height estimation using the ISAF method and the standard CFT method were validated against ground truth estimates and found to be comparable.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".