Rapid Mission Planning for Agile Satellite Earth Observation

To address the mission planning problem for agile satellite earth observation tasks, this study proposes an efficient scheduling method employing a multi-population genetic algorithm (MPGA). Firstly, a fitness function integrating both observation revenue of ground targets and attitude maneuvering energy consumption is constructed. Subsequently, after comprehensive consideration of constraints including observation time windows and attitude maneuver capabilities, the optimization model is formulated. Furthermore, a MPGA framework is developed, featuring collaborative multi-population search strategies that significantly enhance optimization efficiency. Simulation results from typical mission scenario demonstrate that compared with conventional Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO), the proposed method can achieve superior observation revenue with faster convergence speed.