Modelling SAR clutter in multi-resolution radar systems
Bibliographic record
Abstract
The probability distribution function (pdf) used to model Synthetic Aperture Radar (SAR) clutter is an important design element in Constant False Alarm Rate (CFAR) detection; the mean of the local CFAR window is taken as the first moment of the pdf. This study presents research examining the relationship between clutter statistics and radar resolution cell size in the Convair-580 (CV-580) C-SAR and RADARSAT-2 systems. The experiment consisted of decreasing the resolution of a HV polarized, high-resolution, CV-580 sea SAR image and determining the best fit pdf for the corresponding clutter. The same methodology was used on standard- and fine-beam-mode RADARSAT-2 HV images. It was found that the GΓ pdf could be fitted very well to the experimental data for all CV-580 and RADARSAT-2 resolutions. Furthermore, the highest resolution SAR data was Weibull distributed, and decidedly non-Gaussian, in all cases. The medium resolution CV-580 image was very closely modelled by the Lognormal distribution while the Rayleigh distribution (Gaussian statistics) proved highly suitable for modelling the lowest resolution SAR data. The test results presented in this paper may be useful to SAR researchers.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".