Optimal Decision-Making for Constellation Reconfiguration based on a Multi-Stage Integrated Optimization Framework

To address the inadequate coverage of fixed-orbit constellations for emergent tasks, this paper proposes a Multi-Stage Integrated Optimization Framework (M-IOF) that couples discrete task assignment with continuous orbital maneuver planning. In this framework, candidate task chains generated by the scheduler are evaluated through inverse orbit design, and the corresponding feasibility information is fed back to the fitness function. Spatial-functional task clustering is first used to reduce the dimensionality of large-scale heterogeneous tasks. Then, a target- driven inverse orbit solving method maps spatio-temporal constraints into feasible resonant phasing conditions. By considering J 2 perturbations, a one reconfiguration maneuver plan strategy is formulated, enabling satellites to sequentially observe multiple targets through passive drift after one initial maneuver. The scheduling model is solved using an adaptive genetic algorithm with feasibility penalties. Comparative simulations under three task-structure scenarios show that M-IOF improves target completion and task efficiency while reducing unnecessary maneuvering. The results demonstrate the effectiveness of the proposed framework for responsive constellation reconfiguration.