Mission Re-planning for Agile Earth Observation Satellite Using Adaptive Mutation Genetic Algorithm

When the Agile Earth Observation Satellite(AEOS) observes ground target points, the properties of the ground target points often change, which requires the satellite to effectively re-plan its mission in a short time. In order to solve the problem of mission re-planning of the AEOS, an improved genetic algorithm is proposed in this paper. Firstly, the fitness function to be optimized is established according to the satellite's constraints. The benefits of the satellite observation, the constraints of the satellite and the invariant target points included in the re-planned observation sequence is considered in this fitness function. These constraints mainly include time constraints, energy constraints, satellite orbital dynamic constraints, and so on. Secondly, considering the problems faced in the mission re-planning process, such as the satellite's need to complete the mission re-planning in a short time, and the constraints it faces, etc., the Adaptive Mutation Genetic Algorithm(AMGA) is proposed in this paper. Finally, simulation experiments verify that AMGA can complete mission re-planning while meeting various constraints, and that AMGA meets the fast and accurate requirements for solving mission re-planning problems.