Designing Sequences With Minimized Mean Sidelobe Level for Cognitive Radars
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a set of sequences is developed with a minimized mean sidelobe level (MSL). The problem is formulated for cognitive radars using the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm, where the target cognition determines which sidelobes should be suppressed. The cognitive radar configuration requires fast waveform regeneration in each cognition cycle. In this light, the computational burden of the algorithms developed here is revealed to be situated on the singular value decomposition (SVD) operation. The randomization method is adopted to speed up the proposed algorithms. The obtained fast generation of sequences with the desired autocorrelation is key to its utilization in cognitive radars. We also consider two practically important cases and accommodate the proposed approach to them: unimodular and finite-alphabet sequences. The superiority of the developed algorithms is confirmed both in suppression level and speed through extensive numerical simulations.
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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 | 0.001 |
| 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