A new maximum likelihood generalized gamma CFAR detector
Bibliographic record
Abstract
The Generalized Gamma Model has as special cases the Rayleigh, Weibull and Lognormal models. It also closely approximates the K-pdf model. Radar Clutter is often approximated in one of these forms. It is therefore quite useful to develop CFAR (Constant False Alarm Rate) detectors that perform well under this clutter model. In this paper, a Maximum Likelihood Generalized Gamma (MLGG) CFAR detector has been developed. This MLGG detector uses the Maximum Likelihood Equations, both locally and globally, in order to estimate the parameters of the Generalized Gamma clutter. These estimated parameters are then used to estimate the local mean of the detector. The mean of the local CFAR window is then taken as the first moment of the Generalized Gamma distribution evaluated with the estimated parameters. In the examples it is shown that in homogeneous Generalized Gamma clutter, with point targets, the MLGG detector outperforms our standard test detectors, Cell Averager, Ordered Statistic and Optimized Weibull.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".