基于多样本GA-PSO算法的发射入轨段测控设备优化部署

Optimal Deployment of TT&C Equipment in Launch and Orbit Injection Phase based on Multi-sample GA-PSO Algorithm

针对当前航天器发射入轨段地基测控设备部署中存在的效率不高、灵活性不足等问题,考虑最高仰角、地形遮蔽等约束条件,以定轨精度、测控覆盖、资源占用为优化目标,建立给定弹道下测控设备部署优化模型。提出基于多样本遗传-粒子群(Genetic-Particle Swarm Optimization,GA-PSO)算法的发射入轨段测控设备部署优化方法,通过目标权重自适应变换和一定强度的蒙特卡洛仿真实验获取Pareto最优解集,统计分析确定全局最优解。仿真结果表明,该方法可进一步提高发射入轨段定轨精度和测控覆盖率,减少设备冗余,为测控方案制定提供有效数据参考。

Conventional scheme of ground-based TT&C equipment deployment in spacecraft launch and orbit injection phase usually has disadvantages of low efficiency and insufficient flexibility.For those problems,an optimization model for TT&C equipment distribution under a given trajectory is established.Considering maximum elevation,terrain masking and other constraints,the optimization model takes orbit determination accuracy,TT&C coverage and resource occupancy as optimization objectives.To solve this model,an optimization method based on multi-sample Genetic-Particle Swarm Optimization(GA-PSO)algorithm is proposed.Through adaptive weight adjustment and certain intensity Monte Carlo simulation experiments,the Pareto optimal solution set is obtained and a global optimal solution is determined.The simulation results show that the proposed optimization method can further improve orbit determination accuracy,TT&C coverage percentage,and reduce equipment redundancy,which will provide effective data reference for the formulation of TT&C scheme.