Video satellites can stare at target areas on the Earth’s surface to obtain high-temporal-resolution remote sensing videos, which make it possible to track objects in satellite videos. However, it should be noted that the object size in satellite videos is usually small and has less textural property, and the moving objects in satellite videos are easily occluded, which puts forward higher requirements for the tracker. In order to solve the above problems, consider that the remote sensing image contains rich road information, which can be used to constrain the trajectory of the object in a satellite video, this paper proposes an improved Kernel Correlation Filter (KCF) assisted by road information to track small objects, especially when the object is occluded. Specifically, the contributions of this paper are as follows: First, the tracking confidence module is reconstructed, which integrates the peak response and the average peak correlation energy of the response map to more accurately judge whether the object is occluded. Then, an adaptive Kalman filter is designed to adaptively adjust the parameters of the Kalman filter according to the motion state of the object, which improves the robustness of tracking and reduces the tracking drift after the object is occluded. Last but not least, an object tracking strategy assisted by road information is recommended, which searches for objects with road information as constraints, to locate objects more accurately. After the above improvements, compared with the KCF tracker, our method improves the tracking precision by 35.9% and the tracking success rate by 18.1% with the tracking rate at a speed of 300 frames per second, which meets the real-time requirements.

Understanding the dynamic characteristics of solar arrays is important for satellite structural design and attitude control design. Considerable theoretical researches have been carried out towards this problem, but they have not been supported by actual orbit data from outer space yet. In this paper, the on‐orbit vibration characteristic data of solar arrays under the deployment shock are measured by using vibration measurement apparatuses based on structural strain. With these valuable experimental data, more eigenfrequencies of the solar arrays in the microgravity environment are obtained, and the prediction that the vibration of solar arrays will have larger amplitude and longer decay time under vacuum conditions is verified. Moreover, by comparing the eigenfrequency of main vibration under the space condition with that under the ground condition, the accurate value of the system damping ratio is gained, which has an important guideline significant for the structural design of solar arrays.

A miniaturized video satellite can observe the ground targets by recording real-time video clips in staring control mode and therefore obtains a unique advantage over traditional remote sensing techniques. To further extend the application of a video satellite, a strategy for simultaneously observing a group of ground targets is to be developed. To cope with the impacts of an uncalibrated camera on the pointing accuracy which can lead to the failure of a multi-target observation task, an adaptive attitude control method is to be exploited. Hence, to observe multiple ground targets using an onboard uncalibrated camera, this paper proposes an image-based adaptive staring attitude controller. First, a target-selection strategy is proposed to realize a more balanced staring observation of the target group. Second, an updating law is proposed to estimate the camera parameters according to the projection equations. At last, an adaptive staring controller based on the estimated parameters is formulated, so that the center of mass of the ground targets on the image can be controlled towards its desired location, which is normally the image center. The stability of the proposed staring controller is proved using Barbalat’s Lemma. The simulation results show that even though the camera parameters are uncertain, the adaptive control method effectively achieves the staring observation for multiple ground targets by keeping their midpoint at the image center.

Previous staring attitude control techniques utilize the geographic location of a ground target to dictate the direction of the camera’s optical axis, while the assembly accuracy and the internal structure of the spaceborne camera are not considered. This paper investigates the image-based staring controller design of a video satellite in the presence of uncertain intrinsic and extrinsic camera parameters. The dynamical projection model of the ground target on the image plane is firstly established, and then we linearly parameterize the defined projection errors. Furthermore, a potential function and a self-updating rule are introduced to estimate the parameters online by minimizing the projection errors. As the parameters are updating constantly, an adaptive control algorithm is developed, so that the errors between the current and the desired projections of the ground target converge to zero. The stability is proved using Barbalat’s lemma. Simulation results show that the designed controller can successfully move the target’s projection to the desired coordinate even though the camera parameters are unknown.

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%.

Satellite-based target positioning can obtain target location information effectively based on space-based information observation, and plays an indispensable role in disaster relief, hazard monitoring, and related fields. The demand for high-accuracy target positioning has become increasingly prominent with advancements in unmanned driving and precision mapping. In the practical situation, satellites are affected by such as thermal deformation and attitude determination errors, which leads to mixed fixed and random errors in the measured line-of-sight angles, and resulting in the decline of target positioning accuracy. Spurred by this concern, this paper introduces a systematic errors self-correction target positioning method under continuous observations using a single video satellite. First, the error sources and their effects are analyzed, and furtherly a positioning model under satellite observation with the errors are established. Then, in order to achieve accurate target position, an extended-dimension equation is formulated by incorporating target position and fixed biases as the parameters to be estimated. Based on the equation, an estimation strategy combined with initial analytical solutions and iterative refinement is recommended to estimate target position. Specifically, a projection transformation method is proposed to obtain the linearized estimation of the unknowns parameters firstly, then an iterative optimization method is utilized to refine the estimate further. Simulation results demonstrate that, compared with the state-of-the-art algorithms, the recommended algorithm effectively improves target positioning accuracy under non-ideal error conditions.