Autonomous Drone Operator Localization Using UAVs With Reinforcement Learning
Bibliographic record
Abstract
This paper addresses the optimal sensor placement problem (OSPP) for a group of drones to cooperatively localize a drone operator with radio signal detector. The distance from the radio emitter to the radio receiver is modeled by the log-distance path model with white noise. The quality of the localization is described by the Fisher information matrix (FIM). Unlike the static OSPP where the sensors are fixed on the ground, the dynamic OSPP must be considered when putting radio signal sensors on UAVs. The novelty of the paper is the implementation of a reinforcement learning (RL) approach to solving the OSPP using the actor-critic framework. The power consumption model is also used to provide a more realistic constraint on battery usage of the dynamic OSPP. Simulations show that the proposed RL framework can provide an online policy to guide the group of UAVs to achieve the best localization results possible.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".