Clutter Simulation and Compress of ISAR Imaging in Targets on Sea Surface
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Bibliographic record
Abstract
When an inverse synthetic radar(ISAR) imaging the targets on sea surface,sea clutter may decrease the imaging performance.In this paper,we analyze the clutter probability distribution function,and examine the temporal and spatial correlation properties of the clutter.A sea-clutter simulation method is presented,and an algorithm to reject the sea clutter in ISAR signals is proposed.In the simulation method,the K-distribution coherent clutter is generated by spherically invariant random process,followed by two linear transformations,the two dimensional clutter with the given temporal and spatial correlation is obtained.In order to improve the ISAR images quality in presence of sea clutter,we set an appropriate threshold in the ISAR images through the constant false alarm rate processing,hence the scatter signals are retained and sea clutter is rejected.Finally the simulation results demonstrate the effectiveness of the algorithm.
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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