Optimum space-time processing for semi-stationary signals in spatially correlated noise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of optimum space-time processing for multiple Gaussian source signals transmitted through a slowly-varying linear channel and monitored with a passive array of sensors in the presence of spatially correlated noise is addressed. To solve this problem, a new class of linear systems (LS), referred to as semistationary, is introduced. These LS are characterized by time-frequency representations whose variations in time occur over intervals much larger than the corresponding system correlation time. The general conditions under which semistationary LS can be used in array processing are investigated and shown to be satisfied in many applications. By modeling the slowly varying linear channel as a semistationary LS and using the factorization properties of the optimum processor, closed form expressions are obtained for the log-likelihood function of the array output and for the associated Cramer-Rao lower bound on estimator variance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 | 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 it