Enhanced methods for surface current estimation from X-band radar data
Bibliographic record
Abstract
Estimating ocean surface currents accurately is crucial for a wide range of applications, including marine navigation, environmental monitoring, and coastal management. Traditional methods for measuring surface currents face challenges such as limited spatial coverage and sensitivity to environmental noise, making the development of more accurate and robust techniques a pressing need in oceanography. This thesis focuses on improving the accuracy and robustness of ocean surface current estimation using X-band radar image sequences by introducing two novel algorithms. In the first part of this thesis, a Symmetry of Doppler Shifts (SDS) method is introduced for retrieving surface current information from radar images. The method focuses on extracting the wave angular frequencies and corresponding wavenumbers from the radar image sequences. Then, Doppler shifts are calculated based on wavevectors that exhibit symmetry with respect to the origin in the wavenumber plane. These Doppler shifts are used to estimate both the speed and direction of surface currents. Simulations with synthetic data show that the SDS method achieves a root mean square error (RMSE) of 0.13 m/s for current speed and 1.4° for direction. The results indicate that the method performs with accuracy comparable to existing techniques under simulated conditions. The second part of this research builds on the SDS method by integrating it with an enhanced Polar Current Shell (PCS) algorithm. The improvements include the application of Kernel Density Estimation (KDE) for noise filtering, interquartile range filtering to remove outliers, and symmetry-based noise reduction. The modified PCS method also employs a single curve-fitting process, analyzing all wavenumbers in the PCS domain collectively rather than individually. The improved algorithm was validated with both simulated data and real-world radar data from a Decca radar (2008) and a Koden radar (2017). Results show that the modified PCS method reduces the RMSE for speed by 0.06 m/s and direction by 3.8° for the Decca radar, and by 0.02 m/s for speed and 4.6° for direction for the Koden radar, compared to the original PCS method.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".