CFAR detection and extraction of maneuvering air target in strong sea-clutter via time-frequency-based S-method
Bibliographic record
Abstract
In this paper, we present a time-frequency-based detection scheme for the high-frequency surface-wave radar (HFSWR) for the detection of maneuvering air targets in the presence of strong sea-clutter. The performance of the proposed method is evaluated using both synthetic and experimental data. In addition, the proposed time-frequency detection scheme is examined in detail with different signal-to-noise ratio and various examples are considered. The time-frequency-based detection method is then compared with the Fourier-based detector. Results clearly demonstrate that the time-frequency-based detector can significantly improve the detection performance of the HFSWR and add considerable physical insight over what can be achieved by conventional Fourier-based detector currently used by HFSWRs. These results distinctly suggest that the Fourier-based detector is optimal for stationary signals, whereas the Time-Frequency-based detector is optimal for non-stationary signals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".