Bibliographic record
Abstract
Successful processing of Synthetic Aperture Radar (SAR) data requires that the Doppler centroid frequency be accurately estimated. A method for estimating the Doppler centroid for azimuth-offset SAR signals in the presence of noise and speckle is presented. For azimuth-offset SAR systems, changes in the Doppler centroid will cause distortions in the shape of the azimuth spectrum. As a result, the traditional correlation-based estimators will not provide accurate estimates. Doppler centroid estimation based on edge detection provides a practical alternative. The edge detector is tuned to detect a fairly wide, smooth, roof-like type of edge in the signal power spectrum. This method of estimation is evaluated with real data to measure its performance. The results are compared to those obtained by other estimation techniques based on spectrum fitting and energy balance. The accuracy of the edge detection technique over a 2048 azimuth cells by 16 range cells area is found to be 0.02 PRF rms. The edge detection principles can offer a convenient solution for Doppler estimation of range-offset and non-offset SAR signals as well.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".