Design of multiple near‐orthogonal spectrally‐compliant waveforms via alternating successive convex approximations and projections
Bibliographic record
Abstract
The authors consider the design of multiple near‐orthogonal transmit waveforms for a high‐frequency surface wave radar (HFSWR) system operating in a congested spectral environment where the radar must adhere to strict regulations on the interference it can cause to on‐going communication links. The HFSWR application necessitates multiple waveforms to increase the overall unambiguous radar range. Ideally, the waveforms would be constant amplitude, have low autocorrelation sidelobes, and low pulse‐to‐pulse cross‐correlations, all while meeting the imposed spectral constraints; however, it is impossible to know a priori that such waveforms exist. They propose an algorithm based on alternating successive convex approximations and projections to design waveforms with low pulse‐to‐pulse cross‐correlation which meet strict spectral and autocorrelation sidelobe constraints while minimising the amplitude modulation. In the simulation, the proposed algorithm is found to converge rapidly and when compared to similar methods from the recent literature, the proposed algorithm is found to generate waveforms with significantly lower peak‐to‐average power ratios and better pulse compression properties.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".