Blind System Identification in Noise Using a Dynamic-Based Estimator
Bibliographic record
Abstract
In this work we consider the problem of blind system identification in noise driven by an independent and identically distributed (i.i.d) non-Gaussian signal generated from a deterministic nonlinear chaotic system. A new estimator for the phase space volume (PSV) which is a dynamic-based property of chaos is derived using the maximum likelihood formulation. This novel estimator of PSV is denoted as the maximum likelihood phase space volume (ML-PSV). The Cramér Rao Lower Bound (CRLB) of the ML-PSV estimator has also been derived. We have shown that the mean square error of the ML-PSV estimate gradually approaches its CRLB asymptotically. An algorithm is formulated that applies the ML-PSV estimator as an objective function in the task of blind system identification of autoregressive (AR) and moving average (MA) models. The proposed technique is shown to improve blind identification performance at low signal-to-noise ratio (SNR) when the system is driven by both chaotic numeric and symbolic signals. The efficiency of our proposed method is compared with conventional blind identification methods through simulations. Our technique is further validated through experimental evaluation based on a software defined radio (SDR). Results show that the ML-PSV method outperforms the existing blind identification methods producing estimates at a low SNR of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\le20$ </tex-math></inline-formula> dB.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".