Performance analysis of two adaptive radar detectors against non-Gaussian real sea clutter data
Bibliographic record
Abstract
Adaptive radar detection in non-Gaussian clutter is the \nsubject of this work. The performance of two adaptive detection \nschemes developed in the literature, Kelly’s generalized likelihood \nratio test (GLRT) and the adaptive linear-quadratic (ALQ) \ndetector, are tested on real sea clutter data recorded by the \nIPIX experimental radar (McMaster University, Canada) at the \nOsborne Head Gunnery Range (OHGR) in November 1993. The \nresults of first- and second-order statistical analyses performed \non two data sets are reported. Amplitude analysis has been \ncarried out by checking the fitting to Weibull, log-normal, K, and \ngeneralized K models. The results show good agreement between \nperformance prediction based on the generalized K model, with \ntexture strongly correlated among primary and secondary data, \nand the performance obtained by processing the real sea clutter \ndata.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".