A Dynamic Approach to Sensor Network Deployment for Mobile-Target Detection in Unstructured, Expanding Search Areas
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel strategy for the deployment of a static-sensor network based on the use of a target-motion probability model. The focus is on the real-time dynamic and optimal deployment of the network for detecting untrackable targets. The dynamic nature of the deployment refers to the on-line reconfigurability of the network as real-time information about the target becomes available. The optimal locations of the network nodes, in turn, are determined based on maximizing the probability of finding the target through the use of iso-cumulative-probability curves. The proposed strategy is adaptable to unstructured environments with natural terrain variation and the presence of obstacles. Extensive simulations, some of which are included in this paper, verified the advantage of our deployment strategy over other existing methods. Namely, the proposed strategy can tangibly increase the success rate of target detection, while reducing the mean detection time, when compared with uniform-coverage-based approaches that do not consider probabilistic target-motion modeling. A comprehensive example is also included, herein, to illustrate the successful application of our proposed deployment strategy to a wilderness search and rescue scenario, where both static and mobile sensors are employed within a hybrid sensor-deployment strategy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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