基于改进粒子群算法的卫星星座优化设计

Optimization Design of Satellite Constellation Based on Improved Particle Swarm Optimization Algorithm

面向区域覆盖的卫星星座设计是一个多约束的优化问题,提出一种基于改进粒子群(Particle Swarm Optimization,PSO)算法的中轨(Medium Earth Orbit,MEO)卫星星座优化设计方法。以最大化卫星星座对中国区域的平均覆盖率为目标,基于3+4P星座构型进行卫星星座优化设计,并采用改进PSO算法对卫星的轨道参数进行优化。通过仿真软件和卫星仿真工具包(Satellite Tool Kit,STK)互联进行算法验证,并将改进PSO算法、标准PSO算法和遗传算法(Genetic Algorithm,GA)的优化结果进行对比。仿真结果表明,改进PSO算法优化后的卫星星座对中国区域的覆盖率均值为95.34%,分别比标准PSO算法和GA算法高0.68%和9.55%,同时具有更高的平均覆盖重数和总覆盖时长。因此,基于改进PSO算法的卫星星座优化设计方法可以实现较好的覆盖性能。

Regional covering oriented satellite constellation design is a multi-constraint optimization problem.A method of medium earth orbit(MEO) satellite constellation optimization design based on improved particle swarm optimization(PSO) algorithm is proposed.In order to maximize the average coverage of the satellite constellation in China,the optimal design of the satellite constellation is carried out based on the 3+4P constellation configuration,and the improved PSO algorithm is used to optimize the satellite orbit parameters.The algorithm is verified through the interconnection of simulation software and satellite tool kit(STK),and the optimization results of improved PSO algorithm,standard PSO algorithm and genetic algorithm(GA) are compared.The simulation results show that the average coverage rate of the optimized satellite constellation in China is 95.34%,which is 0.68% and 9.55% higher than the standard PSO algorithm and GA algorithm respectively,and has higher average coverage multiplicities and total coverage duration.Therefore,the optimized design method of satellite constellation based on the improved PSO algorithm can achieve better coverage performance.