Constellation configuration design is a prerequisite and critical step in the construction of a mega-constellation system in low Earth orbit. However, the huge number of satellites and the intricate changes in relative positions among them make the configuration design the most challenging part. In this paper, we propose a configuration design scheme for mega-constellations considering collision-avoidance constraints with the objective of uniform global coverage. In this design scheme, the constellation is made up of multiple Walker constellations with the same orbital altitude and different orbital inclination. Moreover, the analytical expression for the minimum distance between any two satellites in the same orbital altitude is derived, and the constellation internal collision-avoidance constraint is established accordingly. Finally, a permanent inter-satellite link design scheme without dynamic reconstruction is presented based on the mega-constellation configuration. Simulation results show that the mega-constellation design scheme introduced in this paper can achieve relatively uniform global coverage (its N Asset Coverage ranges from 18 to 25). The mixed Walker constellation is capable of providing a greater number of N Asset Coverage for most of the world than the Walker constellation of the same satellite order of magnitude. In addition, the inter-satellite link scheme designed in this paper can ensure continuous and stable communication between any satellite nodes.

This paper focuses on the trajectory planning that optical satellites approach the geostationary (GEO) spacecraft and the trajectory planning that the optical satellite flies around the GEO spacecraft for continuous surveillance of the GEO spacecraft The optical satellite approaches the GEO spacecraft with continuous thrust and flies around the GEO spacecraft without thrust. The observation angles of the optical satellite to the GEO spacecraft are less than 30 degrees throughout the entire fly-around mission. Two methods are used in fly-around trajectory planning: trajectory planning based on classical orbital elements and trajectory planning based on CW equations. The advantage of trajectory planning based on classical orbital elements is that the position of the optical satellite relative to the GEO spacecraft is clear in earthcentered-inertial frame and the calculation method of fly-around orbital elements is simple. If the orbital inclination of the GEO spacecraft is less than 0.02 degrees and the semi-major axis of the elliptical relative trajectory of the fly-around formation is greater than 60 km, fly-around trajectory planning based on classical orbital elements is more efficient and easier than that based on CW equations. Optimal control theory is used in the trajectory planning for optical satellites approaching GEO spacecraft and the trajectory optimization is a two-point boundary-value problem. Simulations have proved that this method is feasible, which is highly effective in engineering. This method can be used for space situational awareness and on-orbit servicing missions of GEO spacecraft.

The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.

The economic problem is a primary consideration in mega-constellations design. This work aims to quantify the cost of mega-constellations and analyze the contribution of reusable launch vehicles to saving the mega-constellations cost. In this paper, the cost estimation model of mega-constellation deployment mission is investigated, consisting of the launch cost and satellite cost. Simulation examples demonstrate the high applicability of the cost estimation model and the considerable cost-effectiveness of the reusable launch vehicle in mega-constellation deployment mission.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk–Nelder–Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

The flying around monitoring task of space tumbling target is one of the key links of its on-orbit service. Considering the practical constraints of system inertia uncertainty, external disturbance, actuator saturation, and fault in engineering practice, a robust composite controller based on radial basis function (RBF) neural network is proposed. First, in a new line of sight rotation (RLOS) coordinate system, the relative attitude kinematics and dynamics equations between the tracker and tumbling target based on the error quaternion are established; second, the RBF neural network is used to estimate the additive and multiplicative faults of the system, and the fast nonsingular terminal sliding mode surface (FNTSMS) is combined with the active disturbance rejection control (ADRC) technology to design a finite-time fault-tolerant control (FTC) strategy with high accuracy, strong robustness, and anti-saturation based on the RBF neural network. It is proven that the designed robust fault-tolerant controller can ensure that the system state error converges to a small region containing the origin in a limited time under the Lyapunov framework. Finally, the effectiveness and superiority of the control strategy were verified by numerical simulation.