Application of adaptive joint time–frequency algorithm for focusing distorted ISAR images from simulated and measured radar data
Bibliographic record
Abstract
An adaptive joint time–frequency algorithm has been applied and evaluated for focusing distorted ISAR (inverse synthetic aperture radar) images when the target motion is confined to a two-dimensional plane. It is shown that the adaptive joint time–frequency algorithm provides an effective method of achieving rotational motion compensation for ISAR imaging. Examples provided demonstrate the effectiveness of the adaptive joint time–frequency algorithm with both simulated and experimental ISAR data. Results show that if a target is moving smoothly, standard motion compensation generates a clear image of the target by using the conventional Fourier transform methods. However, when a target performs complex motion such as perturbed random motions, standard motion compensation is not sufficient to generate an acceptable image. In this case, the adaptive joint time–frequency algorithm provides an efficient candidate to resolve the image smearing caused by the time-varying behaviour and leads to a well focused ISAR image when the target motion is confined to a two-dimensional plane. The study also adds insight into the distortion mechanisms that affect the ISAR images of a target in motion.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".