上海交通大学学报

上海交通大学学报 >

2021 , Vol. 55 >Issue 3: 320 - 330

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2019.376

基于NSGA-II算法的编队卫星重构策略

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  • 航天工程大学　研究生院，北京 　101416
孙鸿强(1994-)，男，安徽省马鞍山市人，硕士生，主要研究方向为航天任务分析与设计方向．

收稿日期: 2019-12-26

  网络出版日期: 2021-04-02

基金资助

国家自然科学基金青年基金资助项目(61906213)

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Formation Satellite Reconstruction Strategy Based on NSGA-II Algorithm

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  • Graduate School, Space Engineering University, Beijing 101416, China

Received date: 2019-12-26

  Online published: 2021-04-02

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摘要

针对编队卫星在遭遇空间碎片威胁时的规避决策问题，改进并采用非支配排序遗传算法(NSGA-II)对卫星进行编码，以改进的差分进化算法作为轨道生成模型，以Pareto支配筛选出最优解集，通过引入编队卫星的机动消耗、碰撞概率、工作效率等指标，对卫星的规避轨道进行筛选，保证编队卫星的各项指标得到兼顾．最后以三星编队的海洋侦察卫星为例，引入相位调整、概率计算、水平方向精度因子(HDOP)计算等模型，通过多目标优化算法获得规避轨道的最优解．仿真结果表明，在不同任务目标下，该方法可以更有针对性地制定编队卫星规避策略．

关键词： 编队卫星; 碰撞概率; 多目标优化; 规避策略

本文引用格式

孙鸿强, 张占月, 方宇强 . 基于NSGA-II算法的编队卫星重构策略[J]. 上海交通大学学报, 2021 , 55(3) : 320 -330 . DOI: 10.16183/j.cnki.jsjtu.2019.376

Abstract

Aimed at the problem of avoidance strategy of formation satellites when facing the threat of space debris, a non-dominated sorting genetic algorithm (NSGA-II) is improved and used to code satellites. Besides, the improved differential evolution algorithm is used as the orbital generation model, while Pareto dominance is used to select the optimal solution set. By introducing maneuver consumption, collision probability, work efficiency, and other indicators of formation satellites, the avoidance orbits of satellite are screened to ensure that all the indicators of formation satellites are taken into account. Taking the 3-satellite formation of ocean reconnaissance satellite as an example, the phase adjustment, probability calculation, and horizontal dilution of precision (HDOP) calculation models are introduced. The optimal solution of avoiding orbits is obtained by utilzing the multi-objective optimization algorithm. The simulation results show that this method can formulate a more targeted formation satellite avoidance strategy with different objectives.

Key words： formation satellites; collision probability; multi-objective optimization; avoidance strategy

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