Target Monitoring Capability Analysis for Shipborne HFSWR Under Different Platform Motions
Bibliographic record
Abstract
Compared to shore-based high-frequency surface wave radar (HFSWR), shipborne HFSWR can overcome the constraints of a fixed radar site and extend its detection range. However, the radar echo is influenced by the movement of the shipborne platform, which in turn affects the target monitoring performance of the shipborne HFSWR. In this article, the radar echo model for shipborne radar is introduced, and the Doppler frequency shifts for different signals are given. Then, the characteristics of vessel target echoes for shipborne HF radar under various motion conditions are analyzed. Subsequently, the characteristics of spread sea clutter and its impact on target monitoring under different motion conditions are investigated. Moreover, land clutter, which is often neglected for shore-based HFSWR, is also investigated. Considering the combined effect of clutter blind zones caused by sea clutter and land clutter, the target monitoring capability of shipborne HFSWR under different motion conditions is evaluated, and then, a target monitoring scheme is proposed. In the target monitoring scheme, different navigation scenarios are used to adjust the platform motion state depending on different detection targets. Low-speed navigation scenario is appropriate for the monitoring of moving targets, whereas high-speed navigation scenario is suitable for detecting stationary targets or vessel target initially submerged in nonspread sea clutter. Finally, the clutter extraction results from measured data under different motion conditions and their impact on target monitoring are analyzed, and target monitoring results are provided and validated using field data.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".