Compensating the effects of target acceleration in dual-channel SAR–GMTI
Bibliographic record
Abstract
The authors examine the influence of uncompensated target acceleration on the focusing of moving targets in airborne synthetic aperture radar (SAR) imagery and present one method of detecting and compensating for its effects. Although vehicles travelling on roads and highways routinely experience acceleration, the majority of ground moving target indication algorithms assume a constant velocity scenario, which may result in a defocused target response. Both along-track and across-track accelerations are examined through simulations and experimental data from Environment Canada's airborne CV 580 dual-channel SAR system. Acceleration can have severe effects on focusing and may result in azimuthal shift, azimuthal smearing and a significant loss in peak power in the SAR image. Having determined the effects of acceleration, time–frequency (TF) analysis implementing the pseudo-Wigner–Ville distribution is used to improve target focusing and to detect the presence of significant acceleration. Accelerating targets in experimental airborne data are presented and are identified as such by their non-linear TF histories. Estimations of the instantaneous frequency of the signals yield reconstructed target phase histories, which may be used to identify the presence of certain acceleration components and to obtain a focused image of each target. However, estimates of a target's acceleration and velocity vector may not be uniquely determined using only two receive channels.
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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) | 0.001 | 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) | 0.001 | 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 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".