This article proposes a low-thrust reconfiguration strategy for formation flying based on the Jordan normal form and evaluates its performance based on the required control acceleration and fuel cost. First, the relative dynamics of reconfiguration is disposed by Jordan decomposition to describe the unstable component, i.e., the drift velocity along the track direction, which is not explicit in eigenstructure. Next, the initial states of the Jordan-reduced dynamics are regarded as orbital invariants without control to characterize a formation configuration, similar to the definition of orbital elements that are invariants of the unperturbed Keplerian orbit. Based on the derived differential form of ISJD with control, the reconfiguration trajectory is parameterized by a functional integral, the existence of which is then proved analytically by the proposed polynomial series method. The specified optimal trajectories are yielded by the practical Radau pseudospectral method numerically. Finally, the effects of reconfiguring positions and relative orbital orientation on the required acceleration and fuel cost are investigated using a technique for order preference based on similarity to the ideal solution.

Data clustering analysis is proposed to detect the orbital maneuvers of satellites at different scales. In this study, the unsupervised classification methods of K-means, hierarchical, and fuzzy C-means clustering are used to handle the two-line element (TLE) historical data. The K-means-based contour map method is applied to the characteristic variable selection and cluster number determination. The TLE data of large-, medium-, and small-scale orbital maneuvers are clustered by the aforementioned three methods. Through a series of numerical experiments, it is found that for different scales of orbital maneuvers, the clustering methods have different performances and that they can essentially fulfill the functional requirements of orbital detection. By data mining, the orbital maneuvers of the remote sensing satellites “YAOGAN-9”, “TIANHUI-1”, and “Envisat” can be easily detected, which will provide useful information for further orbital supervision and prediction.

This paper investigates a multi-satellite scheduling method for observing moving targets based on the hybrid heuristic algorithm and observation strip selection method. Firstly, the constraint satisfy problem model for multi-satellite guided collaboration is established considering multiple constraints. Subsequently, based on the genetic algorithm framework incorporated by congestion-based simulated annealing, the enhanced hybrid genetic simulated annealing algorithm is proposed to assign electronic and imaging satellite missions. The uneven distribution of workload among satellites can be balanced by implementing a multi-layer encoding method and neighborhood generation based on congestion, which effectively reduces conflicts and promotes fast convergence. Additionally, for imaging observation strip selection, the short-term track prediction model for moving targets is developed to determine the potential target area, and the area dynamic decomposition is adopted to further locate the specified imaging strip with the highest discovery probability. Finally, numerical results of both hetero-orbit and co-orbit constellations verify the feasibility and effectiveness of the proposed multi-satellite scheduling method.