Distributed receding horizon estimation for sensor networks with improved consensus strategy
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 distributed estimation problem is one of the most essential issues in sensor networks. This paper studies the consensus estimation problem of linear sensor networks based on the distributed receding horizon estimation (RHE) scheme. To design such a consensus estimation scheme, a novel optimization problem is first formulated for each sensor node, by proposing a new consensus strategy. The explicit solution to each optimization problem is provided and the iterative estimation scheme is established. Under the assumption that the communication graph contains a spanning tree, the sufficient conditions for ensuring robust consensus estimation are developed. It is shown that, the estimation error between any two sensor nodes is upper bounded by a value that is related with the energy bound of the sensor noise.
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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 | 0.001 | 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 | 0.000 | 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