Heavy-tailed sea clutter modeling for shore-based radar detection
Bibliographic record
Abstract
Detecting targets embedded in sea clutter poses a clutter-modeling challenge for marine surveillance radar. In this paper, the statistical modeling of sea clutter observed by an X-band high-resolution coastal radar at very low grazing angles (1.05°-1.72°) is investigated. The aim of this paper is to identify the best-fitting statistical distribution to the data with particular attention to the application in the detection scenario. The global goodness-of-fit to the sea clutter distribution and the local fit to only the tail region are both evaluated since the detection probability depends on the whole region of distribution while the detection threshold is mainly determined by the tail region. The results suggest that the Log-logistic distribution is optimal to model the whole region of sea clutter distribution while the recently developed K+Rayleigh distribution, which accounts for thermal noise, fits the tail region best. A general method of calculating the expected probability of detection is also derived to evaluate how the global fit affects the expected probability of detection calculation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".