Fusion Detection of Vessel Target with Multi-dimensional Information for Shipborne HFSWR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it