Object tracking algorithm for UAV autonomous Aerial Refueling

In order to improve the docking success rate in Automated Aerial Refueling (AAR), it is important to identify the receiver aircraft's receptacle for boom receptacle refueling (BRR). Meanshift tracking algorithm only considers the H component color statistics of the target area, lacking spatial information, could easily lead to inaccurate tracking. Besides, Meanshift tracking algorithm could easily lost target under occlusion conditions. To handle these situations, this paper proposes an improved Meanshift tracking algorithm based on color fusion and kernel function combined with Kalman filter (IMS-KF). In view of lacking color component, use RGB linear fusion. In view of lacking spatial information, define the kernel function by setting different weight to pixels, on the basis of the distance from the center point of target to the current point. In view of occlusion conditions, use Kalman Filter algorithm to estimate the location of moving targets. Meanshift tracking results will determine whether use Kalman forecasting. We implemented this algorithm on F-16 simulation experiment platform and the results reveal that our method meets industrial real-time requirements and has a better tracking robustness under a complex environment.