一种基于改进PSO算法的高时间分辨率遥感卫星星座优化设计方法

针对定位、导航、授时、遥感、通信一体的天基信息实时服务系统对遥感信息高时间分辨率获取的需求，提出了基于改进粒子群优化（particle swarm optimization，PSO）算法的遥感卫星星座优化设计方法。基于6N和3+4P星座构型，以重访时间间隔作为优化目标，采用改进的PSO算法对星座优化模型进行求解，分别针对全球覆盖和区域覆盖任务进行了仿真对比试验。仿真结果表明，提出的方法适用于低轨遥感卫星星座设计，满足高时间分辨率要求。

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.