这篇论文给固定翼无人机装上云台相机，用YOLO+UKF做跟踪，加CBF防自遮挡，最后用BPNG精准制导。整套方案仿真验证了效果，挺扎实的。
该研究为配备云台相机的固定翼无人机提出统一控制框架，实现从目标检测到精确拦截的端到端任务。系统采用三阶段策略：基于YOLO的视觉目标获取、基于NMPC的跟踪以及基于BPNG的终端制导。在跟踪阶段，无迹卡尔曼滤波器（UKF）融合YOLO检测与惯性测量，鲁棒估计未知动态目标状态。为防自遮挡，引入含控制障碍函数（CBF）的约束感知NMPC。高保真仿真验证了框架在无人机动力学与相机视场约束下的稳定跟踪与终端命中。
Autonomous Tracking and Terminal Guidance of Moving Targets for Fixed-Wing UAVs
This study introduces a unified control framework for fixed-wing unmanned aerial vehicles (UAVs) fitted with a pan-tilt (PT) camera, intended to perform an end-to-end mission spanning from initial target detection to accurate terminal engagement. The proposed system employs a three-phase strategy: a vision-based target acquisition phase, an NMPC-based tracking phase, and a terminal guidance phase. During tracking, the framework uses an Unscented Kalman Filter (UKF) to fuse YOLO-based visual detections with inertial measurements, enabling robust target state estimation under unknown dynamics. To ensure reliable visual contact, we introduce a constraint-aware Nonlinear Model Predictive Control (NMPC) strategy that incorporates Control Barrier Functions (CBFs) to explicitly prevent UAV self-occlusion -- a common limitation in fixed-wing tracking. Upon satisfying terminal engagement conditions, the system seamlessly transitions control to a quaternion-based Biased Proportional Navigation Guidance (BPNG) law, enforcing precise impact angle constraints. High-fidelity simulations demonstrate that the framework achieves stable, robust tracking and accurate terminal interception while strictly respecting the vehicle's dynamic limits and camera field-of-view constraints.