In autonomous aerial refueling, the circular structure of the drogue refueling port is often used to assist target positioning. Still, the complex background interference and oiled plug obscuring significantly reduce the accuracy of circular feature extraction. To address the background interference problem, an adaptive mean filter is designed to obtain the center of mass of refueling ports to obtain the accurate set of edge points in a smaller range using the imaging operation. To address the oiled plug obscuring problem, an outlier elimination algorithm based on convex hull detection is proposed to enhance the anti-interference performance of feature extraction. An iterative reweighted least squares based on geometric distance is proposed to optimize elliptic targets. On the simulation platform, the influence of K value on the fitting accuracy and efficiency of the iterative reweighted least squares algorithm is emphatically analyzed. At the same time, the accuracy and anti-occlusion performance of the fitting algorithm are tested. The average error of the algorithm is less than 0.5% when there is no occlusion and less than 2% when the occlusion rate is 50%. Finally, the feature extraction experiment of the actual drogue is carried out. Compared with other classical algorithms, the accuracy is improved by 49.3%, the average extraction error is 0.79%, the average processing time is 13.9ms, and the extraction error is controlled within 2% under the special case that the drogue is obscured. Experimental results show that the positioning feature extraction method of drogue meets the requirements of accuracy, rapidity and robustness of image processing for autonomous aerial refueling, and can improve the success rate of autonomous aerial refueling docking and reduce the probability of accidents.