An Optimization Design Method for High Temporal Resolution Remote Sensing Satellite Constellation Based on Improved PSO Algorithm

Satellite constellation design optimization as an important part of the overall design of the remote sensing satellite system, has a decisive influence on the coverage performance of remote sensing satellites. In order to meet the demand for higher temporal resolution of remote sensing information for the space-based real-time information service system, a design method for remote sensing satellite constellation based on improved particle swarm optimization (PSO) is proposed. The improved PSO introduces P_center in the speed and position update inferior particles to enhance the learning between particles and improve the diversity of particles. Based on the 6N and 3+4P constellation configuration, the revisited time interval is used as the optimization goal. The improved PSO is used to solve the constellation optimization model. Four simulation experiments are performed for the global coverage and regional coverage tasks. The effectiveness of the improved PSO is verified by the simulation experiments:① the improved PSO effectively optimizes the temporal resolution of the constellation; ② the improved PSO converges faster than the contrast algorithm and avoids falling into the local optimal solution.The proposed method has good applicability to the design of low-orbit remote sensing satellite constellation, which can meet the temporal resolution requirements of remote sensing satellite constellation in the future PNTRC(positioning, navigation, timing, remote sensing, communication) system.