The Amundsen crater is located in the Aitken Basin of the lunar nearside. Its unique location and the possibility of water ice make it a prime landing area for lunar exploration missions. Constructing a high-quality digital elevation model (DEM) and performing a detailed landing site analysis are critical for research and practical applications. However, this region has many permanently shadowed areas, making optical remote sensing observations impractical. The Lunar Orbiter Laser Altimeter (LOLA) has provided the highest quality and largest satellite altimetry dataset, making it ideal for constructing a high-quality DEM. High-resolution DEMs derived from LOLA data contain significant noise due to the geographic uncertainty of laser altimetry data. This paper utilizes an adaptive iteration method and a filtering method for slope detrending to construct a high-quality DEM of the Amundsen area. Various factors are comprehensively analyzed, including slope, illumination conditions, and temperature. The optimal landing location in the Amundsen area is identified (90.716°E, 84.727°S). The illumination conditions during the landing of the lunar exploration mission are estimated by calculating the obstruction angle of the optimal landing site and the solar altitude angle from 2023 to 2028. The optimal landing time is in October, providing favorable illumination conditions in the coming months. We calculate the maximum range of the azimuth angles (0°-59.5° and 287.5°-360°) that can receive sufficient sunlight at the designated landing site. Our study provides a novel strategy for selecting the placement of solar panels for lunar exploration instruments.

Accurate attitude measurement of non-cooperative space targets is essential for on-orbit servicing, active debris removal, and autonomous rendezvous missions. To address the challenges associated with unknown geometry, rapid tumbling motion, and the limited observability of single-view systems, this study proposes a cooperative multi-view measurement framework for three-dimensional reconstruction and attitude estimation. Multiple spacecraft are deployed to form a stable observation configuration, and multi-view image sequences are acquired to strengthen geometric constraints. A learning-based multi-view stereo reconstruction module is used to estimate depth information and reconstruct point clouds, which are further processed through iterative closest point (ICP) registration to derive inter-frame attitude variations. An extended Kalman filter (EKF) is then introduced to improve temporal consistency and suppress measurement noise. Validation is conducted in a numerical simulation using a simplified Fengyun-1 (FY-1) satellite model under a three-spacecraft cooperative fly-around scenario. The simulation results demonstrate that the proposed method achieves high-precision attitude estimation, with attitude errors below $0.3°$ and positional errors within $0.05\mathrm{m}$. Comparative experiments show that the method maintains stable measurement performance under varying observation distances and viewing configurations. The proposed framework provides a reliable and robust measurement solution for dynamic attitude determination of free-tumbling space targets.

Construction of a high spatial resolution and high precision marine gravity field in coastal areas is constrained by the low quality and sparse coverage of altimetry data, except for limited shipborne and airborne gravity surveys. To address this problem, a mean sea surface height constraint factor (MSSHCF) method based on the ordinary kriging method and the remove-restore technique is proposed from the perspective of interpolation. In this method, the data is standardized during the interpolation process to reduce the error and mean sea surface as variables related to the marine gravity field are added to the semi-variance function in ordinary kriging to obtain a marine gravity field with a spatial resolution of 1′ × 1′. Validation experiments show that the MSSHCF method more closely agrees with the referenced SS V28, DTU17 global marine gravity models than the ordinary kriging method. Our results were further validated against shipborne data; the accuracy of the MSSHCF method is 0.13 and 0.33 mGal higher than that of the ordinary kriging method in two experimental areas. The effects of ocean depth and offshore distance on the results were also assessed. These results show that the proposed method is more accurate than the ordinary kriging method, when the distance and depth varied. Therefore, our study demonstrates that the MSSHCF method is an innovative and feasible tool for extracting gravity fields along coastal, beach, and island areas.

This paper presents a vision-guided nonlinear model predictive control (MPC) framework for coordinated dual-manipulator pre-contact acquisition of tumbling targets. Temporal monocular estimation supplies pose, motion, and uncertainty; rigid-body prediction generates fixture trajectories; and model-based or diffusion candidates initialize exact Denavit–Hartenberg (DH) refinement before clearance-checked execution. Synthetic tests give mean position and attitude errors of 0.0170–0.0182 m and 1.05–1.19°. With initial-state uncertainty and $\pm 6\%$ inertia mismatch, 3 s fixture-prediction root-mean-square error is 0.0822 m with 94.5% covariance coverage. In 30 paired stress scenarios, original-criterion safe capture is 19/30 for exact-DH MPC versus 14/30 for linearized MPC (exact McNemar $p=0.180$). A separate reviewer-motivated post hoc sensitivity imposes three-step angular-rate matching uniformly across five methods: safe capture is 19/30 versus 8/30 (Holm-adjusted $p=0.0171$). Diffusion and uncertainty inflation add no safe captures. Actual-cycle analysis finds a small 1.15% iteration reduction with diffusion, without an end-to-end timing benefit. Sequential mean update times are 1.234 s without diffusion and 1.392 s with diffusion, exceeding the 0.10 s simulated update interval. The evidence concerns delay-free, fixed-base kinematic supervisory planning with target-specific synthetic vision, not real-time hardware control or flight readiness.