An Identification Technique for ARMA Systems in the Presence of Noise
Bibliographic record
Abstract
This paper presents an approach for the identification of minimum-phase autoregressive moving average (ARMA) systems in the presence of additive noise. For the identification of the AR part of an ARMA system, unlike conventional correlation based methods, we propose to employ a once-repeated autocorrelation function (ORACF) of the observed noisy signal which is capable of reducing the effect of additive noise. The ORACF is used in a modified form of the least-squares Yule-Walker equations which provides an estimate of the AR parameters as a least-squares solution. For the identification of the MA part, the residual signal obtained by filtering the observed signal via the estimated AR polynomial is used. In order to tackle the noise in the residual signal, a noise-compensation scheme is proposed. The MA parameters are estimated by using the spectral factorization corresponding to the noise-compensated power spectrum of the residual signal. Simulation results show the superiority of performance by the proposed method in comparison to some of the existing methods at low levels of SNR.
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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 | 0.003 | 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 | 0.001 | 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".