Redundancy-Aware Predictive Control Framework for Multi Camera-Based Localization and Tracking of UAV Swarm
Bibliographic record
Abstract
Counter-UAS operations in sensitive airspaces demand resilient and precise surveillance to mitigate evolving drone threats. This paper presents a novel multicamera PTZ (Pan-Tilt-Zoom) framework tailored for real-time UAV detection, tracking, and localization, emphasizing rapid response and coordinated sensor control. Unlike conventional single-sensor solutions, our architecture orchestrates multiple PTZ cameras via a sophisticated Visual Predictive Controller (VPC) that dynamically allocates resources, optimizes camera orientations, and fuses multiview data. By leveraging 3D triangulation strategies and strategic field-of-view allocation, the system ensures reliable coverage and vision-based tracking of fast-moving or evasive UAVs, even in dynamically changing scenes. Furthermore, in swarm scenarios, the framework addresses the complex challenge of assigning and coordinating PTZ cameras to track multiple UAVs simultaneously as they overlap or converge. Results from simulation experiments with various flight patterns and camera configurations demonstrated the performance and applicability of the proposed system to modern counter-UAS scenarios, highlighting its robustness in high-density aerial environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".