Decentralized sensor selection based on the distributed posterior Cramér-Rao lower bound
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Bibliographic record
Abstract
The paper considers the problem of sensor resource management for distributed, nonlinear tracking applications with the objective of dynamically activating a time-variant subset of observation nodes to optimize the network's performance. The posterior Cramer-Rao lower bound (PCRLB) is a predictive benchmark of the tracker's achievable performance and has recently been proposed as a criteria for sensor selection. Existing PCRLB-based selection techniques are, however, primarily limited to centralized and hierarchical architectures, and when extended to decentralized topologies use approximate expressions [1] for computing the PCRLB. The paper addresses this gap and proposes the distributed PCRLB (dPCRLB) as the sensor selection criteria for decentralized networks without any need for central fusion. We derive an exact expression for computing the dPCRLB and a near-optimal implementation used with the distributed particle filter tracker. Our simulations verify the efficiency of the proposed dPCRLB based sensor selection approach.
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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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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