Weak Signal Estimation in Chaotic Clutter Using Model-Based Coupled Synchronization
Bibliographic record
Abstract
In this paper, detection and estimation of weak signals in chaotic clutter with unknown dynamics are presented. We achieve this in three steps. First, by using Takens' delay embedding theorem and support vector machines (SVMs), the dynamics of the clutter is modeled by training SVMs with a known data set. Second, we augment the model with coupled chaotic synchronization scheme so that a better estimate of the clutter signal can be estimated. Finally, this estimate is subtracted from the observations, and on the residual signal, we apply standard signal detection/estimation techniques. By analyzing the statistical properties of the residual signal, we show that the strong clutter in the observation is replaced by a weakly colored and nonstationary noise. Efficiency of the new estimator is evaluated by computing the mean square error (MSE) of the estimation. Our studies reveal that, by a proper selection of coupling coefficients, we can lower the MSE significantly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".