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| 基于多样本GA-PSO算法的发射入轨段测控设备优化部署 |
| 任猛,刘刚,何兵,李俊瑶,杨阳 |
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| (1.火箭军工程大学 核工程学院,西安 710025;2.宇航动力学国家重点实验室,西安 710043;
3.西安卫星测控中心,西安 710043;2.宇航动力学国家重点实验室,西安 710043;3.西安卫星测控中心,西安 710043) |
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| 摘要: |
| 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。 |
| 关键词: 航天测控 设备部署 多目标优化 多样本遗传-粒子群算法 |
| DOI：10.20079/j.issn.1001-893x.210728002 |
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| 基金项目: |
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| Optimal deployment of TT&C equipment in launch and orbit injection phase based on multi-sample GA-PSO algorithm |
| REN Meng,LIU Gang,HE Bing,LI Junyao,YANG Yang |
| (1.School of Nuclear Engineering,Rocket Force University of Engineering,Xi’an 710025,China;2.State Key Laboratory of Astronautic Dynamics,Xi’an 710043,China;3.Xi’an Satellite Control Center,Xi’an 710043,China) |
| Abstract: |
| 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. |
| Key words: aerospace TT&C;equipment distribution multi-objective optimization multi-sample genetic-particle swarm optimization(GA-PSO) algorithm |
