A two-axis stabilizing gimbal is a device that ensures a sensor is working properly on a moving platform. When classical mechanics (Newton–Euler and Lagrange) is employed to model a two-axis stable gimbal, its limitations can complicate the modeling process. To address this issue, a method for establishing a dynamic model for a two-axis stabilizing platform based on the Kane method is proposed in this paper. The Kane method offers the advantage of a simple model structure and computational efficiency. Initially, utilizing a generalized coordinate system, expressions of the generalized velocities, deflection velocities and angular velocities are derived. Subsequently, the generalized active forces and inertial forces acting on the two-axis stabilized gimbal are analyzed. Finally, by combining force and velocity with the Kane equation, the dynamic model of the two-axis stable platform is obtained, demonstrating the validity of the Kane method for establishing the two-axis stable platform model. To ensure the pointing accuracy stability of the two-axis stabilizing platform, a Novel Particle Swarm Optimization Proportion Integration Differentiation (NPSO-PID) controller is designed using the PSO algorithm. It is then simulated in MATLAB/Simulink and compared with a classical PID controller. Simulation results demonstrate that NPSO-PID exhibits superior object tracking performance compared to classical PID controllers and better optimization of control parameters compared to traditional PSO-PID controllers.

The main functions of the space information system, such as providing the backbone transmission, broadband access, and global connectivity, are realized based on the network topology. Thus, it is necessary to recognize the temporal dynamics of the network topology. A temporal continuity expression method is proposed to describe the topological dynamic characteristics of the network in space information systems. Based on orbit dynamics, a time-dependent adjacency matrix of the space information system can be established by introducing the geometric linkable factor, the link distance intensity factor, and the relative angular velocity factor of the node. The adjacency matrix describes the dynamic characteristics from two layers: one is the physical layer using a time-dependent function, which represents the feasibility of inter-satellite link construction in the system cycle; the other one is the transport layer, described by a piecewise continuous function that varies with time, which characterizes the link quality during the connection period between two satellites. The results show that compared with the existing network topology description methods, the proposed method describes the network topology more accurately, which can distinguish the network topology characteristics at any time, and is more conducive to the understanding and application of the network topology of the space information system.

Image registration is fundamental to UAV remote sensing data processing, but repetitive textures, viewpoint and scale variations, terrain relief, and local deformation can introduce numerous outliers into tentative correspondences. The existing random-sampling and spatial-consistency methods commonly rely on pointwise confidence or isolated pairwise relations and therefore cannot reliably determine whether a locally compatible relation receives stable group-level structural support. To address this limitation, we propose SPGSC++, a second-order spatial guided sampling method for outlier removal. SPGSC++ first uses PCA to reduce the dimensionality of tentative correspondences, thereby reducing the computational burden of subsequent spatial relation construction. It then derives second-order structural quality from the common-neighbor support of directly compatible edges and uses this quality for initial data subset selection and residual-informed data subset refinement to generate and optimize a homography. Comparative experiments show that, on the five datasets CIAP, SUIRD, UAV, ZHANGZHOU, and PAN, the proposed method achieves mean Precision, Recall, F-Score, Runtime, and mean reprojection error values of 0.9627, 0.9797, 0.9702, 0.3724 s, and 1.6454, respectively. Relative to the original SPGSC, SPGSC++ increases the mean F-Score by 0.0391 and reduces Runtime and mean reprojection error by 32.39% and 58.12%, respectively. Overall, the innovation of SPGSC++ lies in using second-order common-neighbor structural quality to guide initial subset selection and residual-informed refinement for robust estimation in UAV remote sensing image registration.