Validation and Evaluation of a Ship Echo-Based Array Phase Manifold Calibration Method for HF Surface Wave Radar DOA Estimation and Current Measurement
Bibliographic record
Abstract
Shore-based phased-array HF radars have been widely used for remotely sensing ocean surface current, wave, and wind around the world. However, phase uncertainties, especially phase distortions, in receiving elements significantly degrade the performance of beam forming and direction-of-arrival (DOA) estimation for phased-array HF radar. To address this problem, the conventional array signal model is modified by adding a direction-based phase error matrix. Subsequently, an array phase manifold calibration method using antenna responses of incoming ship echoes is proposed. Later, an assessment on the proposed array calibration method is made based on the DOA estimations and current measurements that are obtained from the datasets that were collected with a multi-frequency HF (MHF) radar. MHF radar-estimated DOAs using three calibration strategies are compared with the ship directions that are provided by an Automatic Identification System (AIS). Additionally, comparisons between the MHF radar-derived currents while using three calibration strategies and Acoustic Doppler Current Profilers (ADCP)-measured currents are made. The results indicate that the proposed array calibration method is effective in DOA estimation and current measurement for phased-array HF radars, especially in the phase distortion situation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".