The increasing number of space debris and space-active targets makes the space environment more and more complex. Space target recognition, a crucial component of space situational awareness, is of paramount importance to space security. Firstly, this paper elucidates the fundamental principles of ensemble learning, analyzes its characteristics and fusion method, and provides a comprehensive comparison of three common ensemble learning methods. Secondly, this paper analyzes the basic attributes and characteristics of space targets and categorizes the hierarchy of space target recognition. Again, the paper reviews the advances in the application of ensemble learning in space target recognition, focusing on three aspects: space target recognition datasets, the ensemble of traditional machine learning models, and ensemble deep learning. Subsequently, classical machine learning and ensemble learning algorithms are tested on a self-built space target simulation dataset, and we find that Stacking performs well on this dataset. Finally, the paper discusses future research directions.

Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.