Robust Drogue Positioning System Based on Detection and Tracking for Autonomous Aerial Refueling of UAVs

In modern war, endurance mileage and combat radius are important factors for the combat effectiveness of military aircraft. However, the existing aerial refueling schemes mainly use manual docking operations and complex conditions require pilots to have high operating skills. Therefore, this work proposes a visual positioning system for autonomous aerial refueling without adding cooperation marks, which mainly includes a dynamic graph convolution module for drogue detection and a kernel correlation filter for drogue tracking. Firstly, a dynamic GCN module is designed to generate the correlation matrix to aggregate adjacent high-order features and fuse them with global features extracted from the CNN stream to achieve accurate drogue detection. Then, the multi-scale features extracted from the drogue detector network and the HOG features are input to the filter learning module together, and weighted response maps are fused to alleviate the occlusion and scale change problems in the tracking process. In addition, a visual positioning scheme combining a drogue detector and tracker is introduced to output an accurate drogue ROI area. The effectiveness and robustness of the proposed work are verified by comparison with the mainstream methods on COCO detection datasets and real aerial refueling datasets