# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 1 Introduction  
Autonomous navigation technology refers to the ability to determine the position, speed, and attitude of a navigator (such as aircraft, ships, vehicles, robots, etc.) without the help of external navigation devices or signals, using onboard sensors and algorithms. Currently, the application of this technology in the field of aerial refueling has become a hot research topic both domestically and internationally. Aerial refueling requires aircraft to transfer fuel to another or multiple aircraft through fuel hoses during high-altitude flight, significantly extending the flight range. The core challenge of the docking process is to precisely position the refueling port located at the center of the fuel drogue adapter. The crux of this precise positioning is the ability to conduct long-term, stable, and accurate detection and tracking of the target's position at the image level [1]. In the realm of computer vision, target detection involves the processes of scanning, identifying, and localizing targets within images and video scenes. Target tracking can be conceptualized as a \"series of detections\" that captures and follows one or more specific moving targets in real time, noting their patterns of movement and velocities [2].  
As deep learning-based algorithms for target detection and tracking continue to evolve, their performance has proven to significantly surpass that of traditional machine learning, especially in scenarios involving large volumes of data [3]. Nonetheless, as data volumeand sample size expand, the limitations of deep learning begin to emerge. In terms of deep learning in the field of aerial refueling, an excess of drogue samples implies a rapid increase in sample richness, along with an increase in variability between the samples. During aerial refueling, various complex conditions are often encountered, such as high-altitude turbulence, transitions between bright and dark lighting, high-speed motion blur, target appearance deformation, and occlusions. Existing methods are prone to losing track of the target when the refueling drogue is obscured by the aircraft's wings or when the refueling drogue's umbrella-like structure collapses or contracts. There are also cases where, during re-detection, the tanker's engines have a higher confidence level than the refueling drogue.  
Due to the characteristics of long aerial refueling routes, the refueling drogue can undergo large changes in size, background, and shape during the refueling process, which can lead to failures in target detection and tracking. To address this issue, this paper proposes a dynamic measurement system based on adaptive clustering and multi-classifiers. The article is detailed across six sections: Section 1, the Introduction, discusses the research background and purpose; Section 2, Related Work, introduces the current research status, key technical difficulties, and solutions both domestically and internationally; Section 3, Materials and Methods, explains the principles and details of the methods; Section 4, Results, presents the experimental conditions and outcomes; Section 5, Discussion, analyzes and discusses the experimental results and conclusions; Section 6, Future Outlook, introduces the shortcomings of the methods and future research directions.