Collaborative distributed sensor management and information exchange flow control for multitarget tracking using Markov decision processes
Bibliographic record
Abstract
In this paper, we consider the problem of collaborative management of uninhabited aerial vehicles (UAVs) for multitarget tracking. In addition to providing a solution to the problem of controlling individual UAVs, we present a method for controlling the information flow among them. The latter provides a solution to one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. The problem of decentralized cooperative control considered in this paper is an optimization of the information obtained by a number of UAVs, carrying out surveillance over a region, which includes a number of confirmed and suspected moving targets with the goal to track confirmed targets and detects new targets in the area. Each UAV has to decide on the most optimal path with the objective to track as many targets as possible, maximizing the information obtained during its operation with the maximum possible accuracy at the lowest possible cost. Limited communication between UAVs and uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. In order to handle these issues, the problem is presented as an operation of a group of decision makers. Markov Decision Processes (MDPs) are incorporated into the solution. A decision mechanism for collaborative distributed data fusion provides each UAV with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of UAVs that are decentralized, heterogenous, and potentially unreliable. Simulation results are presented on a representative multisensor-multitarget tracking problem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".