An Algorithm for Surface Current Retrieval from X-band Marine Radar Images
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a novel current inversion algorithm from X-band marine radar images is proposed. The routine, for which deep water is assumed, begins with 3-D FFT of the radar image sequence, followed by the extraction of the dispersion shell from the 3-D image spectrum. Next, the dispersion shell is converted to a polar current shell (PCS) using a polar coordinate transformation. After removing outliers along each radial direction of the PCS, a robust sinusoidal curve fitting is applied to the data points along each circumferential direction of the PCS. The angle corresponding to the maximum of the estimated sinusoid function is determined to be the current direction, and the amplitude of this sinusoidal function is the current speed. For validation, the algorithm is tested against both simulated radar images and field data collected by a vertically-polarized X-band system and ground-truthed with measurements from an acoustic Doppler current profiler (ADCP). From the field data, it is observed that when the current speed is less than 0.5 m/s, the root mean square differences between the radar-derived and the ADCP-measured current speed and direction are 7.3 cm/s and 32.7°, respectively. The results indicate that the proposed procedure, unlike most existing current inversion schemes, is not susceptible to high current speeds and circumvents the need to consider aliasing. Meanwhile, the relatively low computational cost makes it an excellent choice in practical marine applications.
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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 | 0.001 | 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 it