The imaging algorithm in the optimal range FrFT domain based on Doppler parameters
Bibliographic record
Abstract
In view of the error impact of Doppler parameters estimation on SAR imaging performance, this paper constructs the energy focusing axis based on the optimal rotation angle in the optimal range FrFT (fractional Fourier transform) domain. On this basis, the relationship among Doppler center frequency, Doppler FM (frequency modulation) rate, and energy focusing axis, as well as the relationship between Doppler center frequency and Doppler FM rate are derived in detail, and then, the basic framework of the D-FrFT-RD algorithm was constructed. The imaging test of Canadian spaceborne RADARSAT-1 measured data shows that when the error of Doppler center frequency estimation gradually increases from 0% to 5%, the energy focusing axis curve may deviate from the normalized value to some extent, but in reality, the impact on SAR imaging performance parameters is not significant. Compared to the traditional RD (range Doppler) algorithm, the energy focusing axis curve corresponding to the D-FrFT-RD algorithm in this paper is relatively stable, with high range resolution, and varying degrees of improvement in both range PSLR and range ISLR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".