In order to realize the reliable autonomous landing of unmanned aerial vehicle (UAV), a visual target composed of multiple sets of concentric circles with different radius ratio is proposed. Considering the distortion of concentric circles after imaging, a recursive circle center detection method that combines with the harmonic ratio constraint composed of circle centers, vanishing points, and intersection points with concentric circles is firstly designed to get the sub-pixel projection point of the center of concentric circles. Compared with Hough circle detection algorithm and other traditional methods, this method can extract the sub-pixel coordinates of the projection point of the circle center more robustly and accurately. After this step, the initial pose is calculated from the correspondence between the image coordinate and platform coordinate, where the correspondence is constructed based on the cross-ratio invariance. The optimized pose is derived from the nonlinear optimization function that is designed based on the conic re-projection model. To eliminate the influence of motion blur images on the decision making during the landing process, a measurement keyframe selection model based on motion continuity is proposed. Furthermore, a multi-mode switching control structure is designed to realize the motion prediction of landing platform, as well as the generation and updating of the spline trajectory of UAV, thereby achieving the autonomous landing of UAV. In 1 500 measurement experiments, the average re-projection error of this measurement method can reach 0. 578 pixel with a variance of 0. 009 6. In the field landing experiments, the positioning error of UAV to the landing platform at the height of 2. 5 m height is less than 3. 5 cm, which indicates that the proposed method has higher visual measurement accuracy and stability, and is able to achieve stable approach and tracking of the landing target and autonomous landing.