Shipborne HFSWR Target Detection in Sea Clutter Regions Based on 3-D Feature Fusion
Why this work is in the frame
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Bibliographic record
Abstract
Shipborne high-frequency surface wave radar (HFSWR) systems face the challenge of sea clutter spreading, which obscures vessel echoes and makes detection difficult. In this article, we propose a novel 3-D target detection algorithm that effectively identifies vessel targets in sea clutter using multidimensional fusion features. The algorithm consists of two stages: 3-D spectrum construction and target detection. In the 3-D spectrum construction stage, the digital narrow beam forming (DNBF) method is combined to transform the range-Doppler (RD) spectrum into a range-Doppler–azimuth 3-D spectrum. In the target detection stage, a two-level cascade target detection algorithm is proposed. At the first level, a 3-D extremum detection algorithm identifies potential vessels in sea clutter from the 3-D spectrum and locates the 3-D tensor blocks containing high-dimensional morphology features of these potential vessels. At the second level, we introduce an intelligent 3-D tensor block classifier, which includes a two-channel 3-D feature-extraction network and a feature classifier. This network extracts 3-D morphology features from the tensor blocks using 3-D discrete wavelet transform and a 3-D convolutional neural network (CNN). The extracted features are then fused using robust sparse linear discriminant analysis (RSLDA), and an extreme learning machine processes the fusion features to produce the final results. The experimental results show that the proposed algorithm outperforms state-of-the-art methods in terms of detection rate and false alarm rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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