A novel Doppler ambiguity resolver based on contrast maximization
Bibliographic record
Abstract
Doppler ambiguity number (DAN) is an important parameter in synthetic aperture radar (SAR) imaging. Erroneous DAN will affect the image quality with lower image signal-to-noise ratio (SNR) and degradation in the system impulse response function. In this paper, a novel Doppler ambiguity resolver based on contrast maximization is proposed. The new algorithm employs candidate DANs to make linear range cell migration correction (RCMC) to the range-compressed data, and check the quality of SAR image obtained by azimuth compression. The estimation value which meets the requirement of maximizing the image contrast will be treated as the final estimation result. Comparing with conventional methods, the new estimator has high precision in the case of both low and high scene contrasts. Experiments with RADARSAT-1 real data are carried out to demonstrate the performance of the proposed approach.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".