Distributed detection with unknown SNR: Separating function and GLRT approaches
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we address distributed detection wherein the instantaneous signal-to-noise ratios (SNRs) at the individual sensors are unknown. A motivating example is a distributed radar receiver when the target radar cross section and/or the noise variance are unknown at each receiver. Recently it has been shown that detection problems can be converted into the estimation of a separating function (SF) followed by comparison to a threshold. Importantly, using an SF eliminates unknown parameters. Here, since the optimal detector depends on the unknown parameters, we propose a Separating Function Estimation Test (SFET) and a Generalized Likelihood Ratio Test (GLRT) at each receiver. Since the likelihood ratio test in the fusion center depends on the detection probability of local receivers, which are unknown, the optimal fusion rule is not applicable. We therefore employ an Asymptotically Optimal SFET (AOSFET) and a GLRT to find a suboptimal fusion rule. We assume that the local SNR at each sensor has a known probability density function. Simulation results show that the SFET outperforms the GLRT in the local detectors and under some conditions, the AOSFET provides better performance as compared to the majority fusion rule.
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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 | 0.001 |
| 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