A comparative study of the LMS adaptive filter versus generalized correlation method for time delay estimation
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Bibliographic record
Abstract
This paper provides a comparison of the LMS adaptive filter versus conventional Generalized Correlation (GC) methods of time delay estimation in terms of their mean-square-error performance. The treatment is restricted to broadband stationary inputs with finite observation time without a priori knowledge of the processor input statistics. The time delay estimator probability distribution, variance, and error probability are derived for the LMS adaptive filter approach. Further, a performance index is given which is optimized with respect to choice of step size. The simulation results presented indicate that without a priori knowledge of the input statistics, both approaches yield similar sub-optimal results. On the other hand, optimal processing of the adaptive filter weights can yield an estimator with variance similar to that of the minimum variance GC method which approaches the Cramer-Rao Lower Bound.
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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