Study of Tracking Algorithm for Autonomous and Precise Landing of Unmanned Aerial Vehicles
To improve the accuracy of autonomous and precise landing of unmanned aerial vehicles and enhance the adaptability of autonomous landing of unmanned aerial vehicles,a new type of unmanned aerial vehicles tracking algorithm is studied.Firstly,the computational process and principle of determining the relative position between unmanned aerial vehicles and landing target are analyzed,and the shortcomings of the traditional landing target tracking algorithm are summarized.Then,the tracking learning detection(TLD)algorithm is innovatively combined with the kernelized correlation filtering(KCF)algorithm in target tracking,and the advantages of the KCF algorithm are used to optimize the TLD algorithm to obtain the TLD+KCF target tracking algorithm.Finally,the optimization algorithm based on unmanned aerial vehicles landing is proposed,and a compared group is set to verify the performance of the algorithm.The comparison results show that the accuracy and success rate of the proposed algorithm exceeds that of the comparison algorithm.The algorithm is highly accurate and strongly stable and can realize autonomous and precise landing of unmanned aerial vehicles.The study helps to improve the accuracy of autonomous and precise landing of unmanned aerial vehicles.
NETL
NSTL
万方数据
