Persistent Tracking using Unmanned Aerial Vehicle: A Game Theory Method
Why this work is in the frame
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Bibliographic record
Abstract
Persistent tracking is of vital importance to a broad range of unmanned aerial vehicle (UAV) missions such that persistent observation and surveillance are indispensable for obtaining uninterrupted measurements of targets of interest. This paper studies the problem of persistent tracking, the goal of which is to design a control strategy for a UAV to keep a moving target in its detection zone, regardless of the target motion. In this paper, persistent tracking problem is formulated in the framework of pursuit-evasion game theory. A bounded and closed region around the UAV in which persistent tracking is feasible is determined rst in this framework. A switching tracking algorithm for the UAV to achieve persistent tracking is then formulated. Simulation results are presented to demonstrate the performance of the proposed tracking algorithm.
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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) | 0.001 | 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