Sensor Placement Effects on Distributed Kalman Filtering in Cyclic Networks
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Bibliographic record
Abstract
This paper investigates the impact of sensor placement on the performance of distributed Kalman filters (DKFs) in cyclic wireless sensor networks (WSNs). We show that in networks with limited sensing coverage, improper sensor placement can significantly degrade estimation accuracy, regardless of the consensus algorithm used. Focusing on the high-pass dynamic average consensus (HP-DAC) protocol, we derive an analytical expression for the filter output for cycle graphs. For cyclic networks, we characterize the locations of zero entries in the eigenvector matrix of the Laplacian and establish design guidelines for optimal sensor placement in both small- and large-scale settings. Theoretical results are validated through simulations, demonstrating that proper sensor placement can significantly enhance DKF estimation performance in sparse WSNs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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