Star sensors are easily disturbed by stray light when working in the space environment: the quality of star image can be seriously reduced and the difficulty of star image preprocessing can be greatly increased. In this paper we propose a novel method for eliminating stray light interference based on the grayscale difference of star images. Firstly, since the grayscale difference between the edge of a star spot and the surrounding background pixels was very large, a grayscale forward difference was performed on the star image row by row. Based on the equivalent difference star image obtained, we could propose an edge pixel criterion for the star spot. Secondly, Stray light interference was eliminated from the star image through a connected component analysis based on the edge pixels criterion for the star spot. The relatively complete star spot areas were marked on the marker map. The simulation results demonstrate that the star spot extraction rate achieved through the proposed method was much higher than that obtained through traditional star image preprocessing methods. As long as the star spot is not submerged by the stray light, the proposed method is robust enough to ensure a successful extraction of the star spot.

With the deepening of degradation, the stability and reliability of the degrading system usually becomes poor, which may lead to random jumps occurring in the degradation path. A non-homogeneous jump diffusion process model is introduced to more accurately capture this type of degradation. In this paper, the proposed degradation model is translated into a state–space model, and then the Monte Carlo simulation of the state dynamic model based on particle filtering is employed for predicting the degradation evolution and estimating the remaining useful life (RUL). In addition, a general model identification approach is presented based on maximization likelihood estimation (MLE), and an iterative model identification approach is provided based on the expectation maximization (EM) algorithm. Finally, the practical value and effectiveness of the proposed method are validated using real-world degradation data from temperature sensors on a blast furnace wall. The results demonstrate that our approach provides a more accurate and robust RUL estimation compared to CNN and LSTM methods, offering a significant contribution to enhancing predictive maintenance strategies and operational safety for systems with complex, non-monotonic degradation patterns.