Vision-Based Tracking Control for a Gimbal in UAV Strike Scenario

In an unmanned aerial vehicle (UAV) strike scenario, vision-based tracking control for a gimbal with a monocular camera is studied in this paper. First, a vision-based localization method is proposed. Image coordinates are converted into geodetic coordinates, and a multi-frame transformation model is established to account for UAV attitude, gimbal angles, and camera parameters. Then, to address the inaccuracy of single-frame observations, a data-driven adaptive covariance Kalman filter (DAC-KF) is introduced to achieve accurate and robust estimation of target positions. After that, a feedforward-proportional velocity gimbal controller based on damped Jacobian inversion is proposed to ensure the camera remains locked on the moving target. Furthermore, a physics-based trajectory model is established to guide the release of unguided projectiles, facilitating accurate strikes on moving targets. Finally, field experiments show that the proposed method effectively locks and strikes moving targets, with a final deviation within one meter.