This paper proposes a pose control scheme of for proximity maneuvering for tracking and observing noncooperative targets with unknown acceleration, which is an important prerequisite for on-orbit operations in space. It mainly consists of a finite-time extended state observer and constraint processing procedures. Firstly, relative pose-coupled kinematics and dynamics models with unknown integrated disturbances are established based on dual quaternion representations. Then, a finite-time extended state observer is designed using the super-twisting algorithm to estimate the integrated disturbances. Both observation field of view and collision avoidance pose-constrained models are constructed to ensure that the service spacecraft continuously and safely observes the target during proximity maneuvering. And the constraint models are further incorporated into the design of artificial potential function with a unique minimum. After that, the proportional–derivative-like pose-constrained tracking control law is proposed based on the estimated disturbances and the gradient of the artificial potential function. Finally, the effectiveness of the control scheme is verified through numerical simulations.

Global horizontal irradiance (GHI) is a key parameter for estimating solar background noise in Geiger-mode single-photon-counting LiDAR operating on a moving airborne platform, where direct irradiance measurement increases system size, weight, and cost and may be affected by platform motion. To enable efficient GHI prediction from meteorological variables, we propose a two-stage feature optimization method based on relevance and redundancy. First, first-order linear and second-order nonlinear fitting are combined to evaluate feature relevance; then, a recursive variance inflation factor (VIF)-based procedure is used to remove redundant variables. The method is evaluated on the NSRDB and NBSC public datasets using six prediction models, including LSTM, CNN, RNN, RFR, SVR, and MLP. Averaged over the two datasets and six prediction models, the proposed method achieves MAE, RMSE, $R2$, RRMSE, RSR, and WI values of 0.0220, 0.0351, 0.9683, 0.2042, 0.1533, and 0.7215, corresponding to improvements of 9.06%, 9.22%, 0.21%, 6.97%, 11.85%, and 0.23%, respectively, over the corresponding second-best competing results. A field experiment is further conducted as an auxiliary validation, yielding average improvements of 5.60%, 5.42%, 5.14%, 5.80%, 1.05%, and 0.90% for the same six metrics relative to the corresponding second-best results among VIF, Pearson, ARD, NGA, WRFE, PI, and Boruta.