A Novel Vision-Based PRPL Multistage Image Processing Algorithm for Autonomous Aerial Refueling

Autonomous aerial refueling (AAR) technology can increase the flight endurance of unmanned air vehicles (UAVs) effectively. Drogue detection and target tracking method are significant for probe-drogue refueling system in the docking stage. This paper proposes a novel vision-based multistage image pr...

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Bibliographic Details
Published in:Wireless communications and mobile computing Vol. 2021; no. 1
Main Authors: Wu, Ling, Sun, Yongrong, Zhao, Kedong, Fu, Xiyu
Format: Journal Article
Language:English
Published: Oxford Hindawi 2021
John Wiley & Sons, Inc
Subjects:
Adaptive algorithms
Aerial refueling
Aircraft
Algorithms
Image processing
Image resolution
Image segmentation
Light emitting diodes
Support vector machines
Target detection
Tracking
Unmanned aerial vehicles
Vision systems
ISSN:1530-8669, 1530-8677
Online Access:Get full text
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Summary:Autonomous aerial refueling (AAR) technology can increase the flight endurance of unmanned air vehicles (UAVs) effectively. Drogue detection and target tracking method are significant for probe-drogue refueling system in the docking stage. This paper proposes a novel vision-based multistage image processing algorithm of drogue detection and target tracking for AAR. This algorithm divides the whole task into four stages: preprocessor, recognizer, predictor, and locker (PRPL). The adaptive threshold segmentation (ATS) algorithm and support vector machine (SVM) classifier are utilized in preprocessor and recognizer for drogue detection. An improved kernelized correlation filter (IKCF) tracking algorithm and scale adaptive method by window position as well as image resolution adjusted are adopted in predictor and locker for target tracking in complex dynamic environments. Finally, the proposed PRPL multistage image processing strategy is tested using an autonomous aerial refueling testbed. The results indicate that the proposed algorithm achieves high precision, good reliability, and real-time capability compared with conventional algorithms. The average processing time is within 11 ms in various environments, which can meet the requirement for drogue detection and tracking in AAR.
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ISSN:1530-8669
1530-8677
DOI:10.1155/2021/2778857