Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar
Why this work is in the frame
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Bibliographic record
Abstract
Due to high false alarm rate and low positioning accuracy of compact high-frequency surface wave radar in moving vessel detection, false plot-to-track association often occurs during moving vessel tracking, thus leading to track fragmentation and false tracking. In order to address this problem, a plot quality evaluation method is proposed and applied to plot-to-track association. Firstly, the differences in spatial correlation of echo spectrum amplitudes and position among moving vessels, clutters, and noise on a range-Doppler map are analyzed, and a plot quality index integrating multi-directional gradient, local variance, and plot position probability is developed. Then, the plots labeled as low quality are removed to reduce both the negative impact of false alarms on plot-to-track association and the computational burden. Eventually, both plot quality index and kinematic parameters are used to calculate the association cost and determine the plot-track pairs during the plot-to-track association procedure. Experimental results with field data demonstrate that the proposed plot quality index can effectively distinguish moving vessel and other plots. Compared with both the nearest neighbor data association method and the joint probability data association method, the association accuracy of the proposed method is greatly improved and, thus, the tracking continuity is enhanced in dense clutter scenarios.
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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