A Vessel Azimuth and Course Joint Re-Estimation Method for Compact HFSWR
Why this work is in the frame
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Bibliographic record
Abstract
Small-aperture compact high-frequency surface wave radar (HFSWR) suffers from low azimuth accuracy for target detection due to its wide beamwidth. Multitarget tracking (MTT) algorithms, when applied to the raw target detection data of HFSWR, fail to effectively filter the target azimuths, and thus, resulting in inaccurate target tracks and courses. In this article, a vessel azimuth and course joint re-estimation method by exploring Doppler velocity and the information accumulated from consecutive observations is presented. It begins with applying an MTT algorithm to a measured target states data sequence acquired by HFSWR to establish initial target tracks, from which the measured range, azimuth, and radial velocity data sequences are obtained. Then, the azimuth trend is extracted from the obtained azimuth data sequence as roughly corrected azimuth estimates, with which the target locations are roughly corrected. Subsequently, target speeds and initial courses are estimated based on the roughly corrected location data sequence, followed by a data selection procedure based on proposed control parameter rules to select the qualified data for calculating the projected angles in terms of speed and direction, separately. Eventually, the target azimuth data sequence is further refined using a linear azimuth error model, whose parameters are obtained by minimizing the difference between the projected angles using a constrained optimization method. Experimental results from field data demonstrate that the proposed method can estimate the target azimuths with significantly improved accuracy. The deviations of the corrected target locations are considerably reduced, and the accuracy of course estimation is enhanced.
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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