Fuzzy control of multi-scale target tracking for quadrotor drones
Bibliographic record
Abstract
Quadrotor drones face multiple challenges such as accuracy and real-time performance when tracking targets in a constantly changing and dynamic environment. To improve the target tracking accuracy and flight control stability of quadrotor drones in dynamic scenes, a quadrotor drone control method combining multi-scale target tracking algorithm and Type-2 fuzzy control is proposed. Firstly, a multi-scale object detection method based on kernel correlation filter is adopted, which can effectively cope with target scale and position changes through multi-scale analysis. Second, employing Type-2 fuzzy control to handle uncertainties in the control process ensures that the quadcopter drone accurately adjusts its flight state during target tracking. Experimental results show that the accuracy of the multi-scale object detection method based on kernel correlation filter is 0.97 on 1600 datasets. In terms of the comprehensive performance of target tracking and flight control, the accuracy of the Type-2 fuzzy control model is 0.92, the precision is 0.91, the recall rate is 0.91, the F1 value is 0.90, and the area under the curve value reaches 0.93, demonstrating strong target tracking ability and control accuracy. Experimental results show that the proposed multi-scale target tracking fuzzy control for quadrotor drones has excellent performance, providing a reliable control scheme for the application of quadrotor drones in complex dynamic 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".