An Enhanced Frequency Estimation Algorithm Using a Three-Point Spectral Interpolation Method
Bibliographic record
Abstract
Frequency estimation of sinusoidal signals is a critical task in various signal processing applications, including control systems, monitoring, radio broadcasts, and more.The Fast Fourier Transform (FFT) is a widely employed technique for signal analysis; however, it suffers from spectral leakage issues.To mitigate this problem, windowing functions are utilized, aiming to enhance frequency estimation accuracy through the combination of an optimal window and a precise frequency correction formula.In this study, a novel frequency estimation algorithm based on a three-point spectral interpolation method is proposed and compared with the Jacobsen algorithm.Simulation results demonstrate that the proposed algorithm exhibits superior performance in terms of frequency estimation errors.Specifically, the maximum frequency estimation error for the proposed algorithm, when using the Nuttall window, was found to be 0.001, representing a 29-fold reduction compared to the error of 0.029 for the Jacobsen algorithm.This improvement highlights the effectiveness of the proposed interpolation-based algorithm for accurate frequency estimation in signal processing applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".