Optimal Time-Frequency Distribution for Instantaneous Frequency Estimation of Signals With Known IF Patterns
Bibliographic record
Abstract
In this paper, we optimize the kernel of a TimeFrequency Distribution (TFD) for a specific class of signals with some Instantaneous Frequency (IF) patterns, such as Linear Frequency Modulated (LFM) and Sinusoidal Frequency Modulated (SFM) signals. We introduce an IF estimator of such patterns by maximizing the integration of the Local Signal-to-Noise Ratio (LSNR) in the Time-Frequency (TF) domain over the curves of possible patterns by using an initial kernel. We then maximize the integrated LSNR by optimizing the kernel function by using the estimated IF. This approach provides a signal-dependent kernel in the Time-Delay Domain (TDD), which can be used to re-estimate the IF more accurately. Our experimental results for linear and sinusoidal frequency modulation reveal that our optimized signaldependent TFD kernel significantly outperforms well-established TFDs in the IF estimation, while significantly reducing the number and impacts of the cross-terms (CTs), especially for crossing component signals
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".