无人机（UAV）通过实时环境感知与规划，可实现在障碍场景中对移动目标的跟踪，但在密集障碍下跟踪急转大机动目标时，仍存在响应滞后、目标遮挡与障碍碰撞风险高的问题。对此，提出一种能见性增强的急转大机动目标跟踪分层轨迹规划方法。前端路径规划构建融合历史与实时目标轨迹的启发函数，以结合目标实时运动状态，提高UAV跟踪的响应速度。后端轨迹优化融入目标与障碍能见性约束以实现稳定跟踪，具体包括：基于椭球序列构建视场（FOV）模型以提升遮挡检测准确性；根据观测到的障碍密度自适应调整跟踪距离，以提高目标能见性；约束UAV视线与运动方向的夹角，提高障碍的持续能见性以降低碰撞风险。仿真试验表明，相比于vis-planner与fast-tracker，本文方法可提升目标跟踪成功率，在密集障碍环境下对急转大机动目标的跟踪成功率可达80%以上。

Unmanned Aerial Vehicles （UAVs） can achieve tracking of moving targets in obstacle environments through real-time environmental perception and planning. However， for sharp-turning and large-maneuvering target tracking in dense-obstacle environment， there are still some problems， such as response lag， target occlusion and high risk of obstacle collision. To address this issue， a hierarchical trajectory planning method for visibility-enhanced tracking of sharp-turning and large-maneuvering target is proposed. The front-end path planning constructs a heuristic function that integrates the target’s historical and real-time trajectories to incorporate the target's real-time motion state and improve the response speed of tracking UAV. The back-end trajectory optimization incorporates visibility constraints of the target and obstacles to achieve stable tracking. At the back-end， a sequence of ellipsoids is used for modeling Field of View （FOV） to improve detection accuracy of occlusion. And the tracking distance is adaptively adjusted according to the seen obstacle density to enhance target visibility. Besides， the angle between the UAV’s line of sight and UAV’s motion direction is constrained to improve continuous visibility of obstacles for reducing collision risk. Simulation tests show that the proposed method improves the tracking success rate compared with the methods of vis-planner and fast-tracker， and it can achieve a success rate of over 80% for tracking of sharp-turning and large-maneuvering target in dense-obstacle environment.