Fusion Detection of Vessel Target with Multi-dimensional Information for Shipborne HFSWR
Bibliographic record
Abstract
Compared to shore-based high frequency surface wave radar (HFSWR), shipborne HFSWR offers advantages such as platform mobility and flexibility, as well as the ability to expand the detection coverage without being constrained by the location of shore-based stations. However, there are several challenges in target detection using shipborne HFSWR: first, due to the size limitations of the shipborne platform, the radar array and transmission power are small, resulting in weak target echo signals; second, the forward motion of the shipborne platform causes the first-order sea clutter to broaden, leading to some vessel target echo signals falling into the broadened sea clutter; third, the platform's maneuvering can also cause the broadening of target echoes, further reducing their signal-to-noise ratio (or signal-to-clutter ratio). To address these issues, this paper proposes a fusion detection method for shipborne HFSWR targets based on multi-dimensional information. Initially, target detection is performed separately in individual dimensions such as the range-Doppler (RD) spectrum, time-frequency (TF) spectrum, and azimuth-Doppler (AD) spectrum. Subsequently, the detection results from different dimensions are integrated using a two-level fusion strategy, which involves fusing of the detection results on the TF and AD dimensions at the same range, followed by fusing these results with the RD dimension detection results. This approach enhances the target detection performance of shipborne HFSWR under complex conditions. Finally, the method is validated using simulation and real measurement data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".