Compared to traditional remote sensing technology, video satellites have unique advantages such as real-time continuous imaging and the ability to independently complete staring observation. To achieve effective staring control, the satellite needs to perform attitude maneuvers to ensure that the target’s projection stays within the camera’s visual field and gradually reaches the desired position. The generation of image-based control instructions relies on the calculation of projection coordinates and their rate of change (i.e., visual velocity) of the projection point on the camera’s image plane. However, the visual velocity is usually difficult to obtain directly. Traditional calculation methods of visual velocity using time differentials are limited by video frame rates and the computing power of onboard processors, and is greatly affected by measurement noise, resulting in decreased control accuracy and a higher consumption of maneuvering energy. In order to address the shortcomings of traditional calculations of visual speed by time difference methods, this paper proposes a control method based on the estimation of visual velocity, which achieves real-time calculation of the target’s visual speed through adaptive estimation; then, the stability of the closed-loop system is rigorously demonstrated. Finally, through simulation comparison with the traditional differential method, the results show that the proposed method has an improvement in attitude accuracy for about 74% and a reduction in energy consumption by about 77%.

Video satellites feature agile attitude maneuverability and the capability for continuous target imaging, making them an effective complement to ground-based remote sensing technologies. Existing research on video satellite tracking methods generally assumes either accurately calibrated camera parameters or precisely known target positions. However, deviations in camera parameters and errors in target localization can significantly degrade the performance of current tracking approaches. This paper proposes a novel adaptive visual tracking method for video satellites to track near-circular space targets in the presence of simultaneous uncertainties in both camera parameters and target position. First, the parameters representing these two types of uncertainties are separated through linearization. Then, based on the real-time image tracking error and the current parameter estimates, an update law for the uncertain parameters and a visual tracking law are designed. The stability of the closed-loop system and the convergence of the tracking error are rigorously proven. Finally, quantitative comparisons are conducted using a defined image stability index against two conventional tracking methods. Simulation results demonstrate that under coexisting uncertainties, traditional control methods either fail to track the target or exhibit significant tracking precision degradation. In contrast, the average image error during the steady-state phase exhibits a reduction of approximately one order of magnitude with the proposed method compared to the traditional image-based approach, demonstrating its superior tracking precision under complex uncertainty conditions.