A Method of ISAR Imaging for Missile with Fake Target Interference
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Bibliographic record
Abstract
A process of ISAR imaging for missile with fake target interference presents more challenges than for a single target. In this paper, a method based on the Keystone transform is proposed for dealing with a multi target echo signal. This approach removes radial velocity for all targets by estimating information from narrow band echo signal. Then, a Keystone transform, which carries the inherence of closely relevant to the central frequency, is used to compensate the relative speed remained on missile at the equivalent central frequency and to efficiently implement envelope aligning for the desired target while the doppler frequency of missile is undersampled. Third step of the method separates the rough missile image from range doppler plane where targets do not overlap each other. The last step applies further phase compensation algorithm to the separated single targets echo signal to develop a final fine target image. The effectiveness of this algorithm is testified by processing of emulational data.
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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 it