Cascaded Detection Method for Ship Targets Using High-Frequency Surface Wave Radar in the Time–Frequency Domain
Bibliographic record
Abstract
Compact high-frequency surface wave radars (HFSWRs) utilize miniaturized antennas, resulting in lower antenna gain and a reduced signal-to-noise ratio (SNR) for target echoes. Due to noise interference, ship echoes in the noise region often fall below the detection threshold, leading to missed detections. To address this issue, this paper proposes a cascaded detection method in the time–frequency (TF) domain to improve ship detection performance under such conditions. First, TF features are extracted from TF representations of ship and noise signals. Supervised machine learning algorithms are then employed to distinguish targets from noise, reducing false alarms. Next, a non-constant false alarm rate (CFAR) threshold is computed based on the noise mean, standard deviation, and an adjustment factor to improve detection robustness. Experiments show that the classification accuracy between the ship and noise signals exceeds 99%, and the proposed method significantly outperforms the conventional CFAR and TF-domain CFAR in terms of detection performance.
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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 | 0.001 | 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".