A multi-objective optimization framework of constellation design for emergency observation

To meet the urgent needs of emergency observation missions, a well-designed constellation is key to ensuring system performance. This paper presents a multi-objective optimization framework which is well suited for constellation designers to identify key trade-offs and make decisions. Firstly, this work investigates the metrics of responsiveness and coverage simultaneously to approximate to the actual conditions of emergencies, and a multi-objective mathematical model is constructed to explore solutions to enhance the overall system performance. Then, according to the characteristics of emergency missions, a hierarchical chromosome encoding method is proposed in the framework to encode a solution that explores different sized constellations without constraining its specific geometry. Finally, we propose the use of radial axes plots to help constellation designers to gain pragmatic insights for informed decision making. The simulation of disaster management verifies that the proposed chromosome encoding method effectively promotes the convergence of the algorithm, and the multi-objective optimization framework helps to generate a rapid and adequate observation satellite constellation to provide maximum coverage performance while ensuring timeliness.