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Record W6891589664 · doi:10.48336/d24j-bk13

Enhanced methods for surface current estimation from X-band radar data

2025· article· en· W6891589664 on OpenAlexaff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRobustness (evolution)RadarWavenumberDoppler effectNoise (video)Wave radarRadar imagingDoppler radar

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.329
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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