Three-dimensional (3D) reconstruction technology for space targets can provide information support such as target structures and dimensions for space missions including on-orbit services and fault diagnosis, which is crucial for maintaining the normal operation of space assets. Optical devices and ISAR (Inverse Synthetic Aperture Radar) can provide high-resolution two-dimensional (2D) images of space targets, serving as the primary means for space target observation. However, existing 3D imaging methods for space targets exhibit significant limitations: the fusion process of optical observation data and ISAR observation data lacks automation, and factors such as image offset that affect 3D imaging quality are not fully considered. To address these issues, this paper proposes a novel 3D imaging method for space targets utilizing optical-ISAR joint observation. This method first employs semantic segmentation networks to automatically extract target regions from optical and ISAR images. Then, it combines octree-space carving technology for efficient 3D reconstruction and performs correction of target region offset based on projection optimization to achieve high-quality 3D imaging. This method eliminates the need for manual target region extraction, improving the automation level of the algorithm. The application of octree-space carving technology greatly enhances the efficiency of 3D reconstruction. Moreover, by correcting target region offset, the proposed method delivers superior 3D imaging results. Simulation experiments demonstrate that the method exhibits significant superior performance in terms of reconstruction efficiency and imaging quality. Additionally, experiments based on measured data further verify the feasibility and practical application value of the proposed method.

In this paper, a trajectory optimization method without an initial value guess is proposed. The method employs the Lagrange multipliers from the nonlinear programming process to estimate the costate of the optimal control problem. It utilizes a homotopic process to address the minimum-fuel problem. The estimated costate serves as a useful initial guess for the indirect shooting method, mitigating the initial value sensitivity. The sequential quadratic programming process used in the shooting process avoids the non-optimal results of the direct method. The minimum-time and minimum-fuel low-thrust rendezvous problems on cislunar L1-vicinity, L2-vicinity, and L2-south near rectilinear halo orbits are solved in this paper. The numerical results demonstrate that using low-thrust propulsion can reduce fuel consumption by 42.36% to 84.62% compared with traditional two-impulse maneuvers in the circular restricted three-body rendezvous problem.