Kalman Filter Disciplined Phase Gradient Autofocus for Stripmap SAR
Bibliographic record
Abstract
The phase gradient autofocus (PGA) and its improvements have been aimed to estimate the phase error exclusively from the phase of raw data. In this article, we introduced the Kalman filter (KF) into stripmap PGA (or phase curvature autofocus) by taking advantage of the continuous movement of the aircraft. The fundamental principle is to build a kinematic model and a measurement model to predict the phase curvature of the next subaperture, and to correct the measurement (phase curvature) of the current subaperture. The advantages of employing KF are as follows: 1) the inaccurate PGA estimation due to wrong target selection, serious phase error, or low signal-to-clutter ratio can be corrected by a well-maintained KF; 2) the prediction of the KF can be applied to the data of the next subaperture before phase estimation, to decrease the algorithm converge time, and to increase the estimation accuracy; and 3) KF disciplined PGA naturally fits the sequential processing needs and is capable of generating good phase error estimation in one execution. This helps real-time synthetic aperture radar (SAR) autofocus and motion compensation. The disciplining of the autofocus using KF is not restricted to PGA-based algorithm. It can be applied to other subaperture-based autofocus algorithms.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".