Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

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

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

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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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

Cite this article

SUN Hongqiang, ZHANG Zhanyue, FANG Yuqiang . Formation Satellite Reconstruction Strategy Based on NSGA-II Algorithm[J]. Journal of Shanghai Jiaotong University, 2021 , 55(3) : 320 -330 . DOI: 10.16183/j.cnki.jsjtu.2019.376

References

[1] SKANDEROVA L, FABIAN T, ZELINKA I. Differential evolution dynamics modeled by longitudinal social network[J]. Journal of Intelligent Systems, 2017, 26(3): 523-529.
[2] ZHANG J R, ZHAO S G, ZHANG Y, et al. Optimal planning approaches with multiple impulses for rendezvous based on hybrid genetic algorithm and control method[J]. Advances in Mechanical Engineering, 2015, 7(3): 1-11.
[3] FLETCHER K. Space debris: The ESA approach[M]. Paris, France: European Space Agency, 2017: 2-3.
[4] PHILLIP A M. Orbital debris quarterly news [EB/OL]. (2019-08-01)[2019-09-15]. .
[5] 汉京滨，张雅声，汤亚锋，等. 基于NSGA-II 的通信星座重构方法研究[J]. 兵器装备工程学报，2019, 40(8): 74-79.
[5] HAN Jingbin, ZHANG Yasheng, TANG Yafeng, et al. Research on reconstruction method of communication constellation based on NSGA-II[J]. Journal of Sichuan Ordnance, 2019, 40(8): 74-79.
[6] BANG J, AHN J. Two-phase framework for near-optimal multi-target Lambert rendezvous[J]. Advances in Space Research, 2017, 61(5): 1273-1285.
[7] 杨震，罗亚中，张进. 近地快速交会调相策略设计与任务分析[J]. 国防科技大学学报，2015, 37(3): 61-67.
[7] YANG Zhen, LUO Yazhong, ZHANG Jin. Analysis and design of phasing strategy for near-earth short rendezvous mission[J]. Journal of National University of Defense Technology, 2015, 37(3): 61-67.
[8] 张雅声，张育林. 性能修复型星座快速重构方法研究[J]. 装备指挥技术学院学报，2005, 16(4): 66-72.
[8] ZHANG Yasheng, ZHANG Yulin. Research on the fast reconfiguration method for the performance restore type constellation[J]. Journal of the Academy of Equipment Command & Technology, 2005, 16(4): 66-72.
[9] FRITSCHE M, SOAS'GNICA K, RODRíGUEZ-SOLANO C J, et al. Homogeneous reprocessing of GPS, GLONASS and SLR observations[J]. Journal of Geodesy, 2014, 88(7): 625-642.
[10] GAVIN R T. NASA’s orbital debris conjunction assessment and collision avoidance strategy [R]. Houston, TX, USA: NASA Johnson Space Center, 2010: 20100005139.
[11] 霍俞蓉. 空间碎片碰撞预警的误差分析方法研究[J]. 中国科技纵横，2016(7): 230-231.
[11] HUO Yurong. Research on error analysis method of space debris collision warning [J]. China Science & Technology Panorama Magazine, 2016(7): 230-231.
[12] CHAN K. Collision probability analyses for earth orbiting satellites[J]. Pacific Basin Societies, 1997: 1033-1048.
[13] 刘家祥. 航天电子侦察定位技术理论研究与仿真软件设计[D]. 南京： 东南大学，2017.
[13] LIU Jiaxiang. Research in aerospace electronic reconnaissance location technology and simulation software design[D]. Nanjing: Southeast University, 2017.
[14] 张淼艳，张军，朱衍波. 卫星导航系统HDOP和VDOP的研究[J]. 遥测遥控，2009, 30(2): 6-12.
[14] ZHANG Miaoyan, ZHANG Jun, ZHU Yanbo. Research on the HDOP and VDOP of satellite navigation systems[J]. Journal of Telemetry, Tracking and Command, 2009, 30(2): 6-12.
[15] 王海丽. 军用侦察卫星星座技术研究[D]. 长沙： 国防科学技术大学，2001.
[15] WANG Haili. Research on military reconnaissance satellite constellation technology [D]. Changsha: National University of Defense Technology, 2001.
[16] 张守信. GPS卫星测量定位理论与应用[M]．长沙： 国防科技大学出版社，1996: 90-91.
[16] ZHANG Shouxin. Theory and application of GPS satellite measurement and positioning[M]. Changsha: National University of Defense Technology Press, 1996: 90-91.
[17] 呼忠权. 差分进化算法的优化及其应用研究[D]. 秦皇岛： 燕山大学，2013.
[17] HU Zhongquan. The optimization of differential evolution algorithm and its application research[D]. Qinhuangdao: Yanshan University, 2013.
[18] FEOKTISTOV V, JANAQI S. Generalization of the strategies in differential evolution[C]∥18th International Parallel and Distributed Processing Symposium, 2004. Proceedings. Piscataway, NJ, USA: IEEE, 2004: 165.
[19] CANALES-BUSTOS L, SANTIBA?EZ-GONZáLEZ E, CANDIA-VéJAR A. A multi-objective optimization model for the design of an effective decarbonized supply chain in mining[J]. International Journal of Production Economics, 2017, 193: 449-464.
[20] MICHALAK K. Evolutionary algorithm with a directional local search for multiobjective optimization in combinatorial problems[J]. Optimization Methods and Software, 2016, 31(2): 392-404.
[21] 赵双，张雅声，戴桦宇. 基于差分进化算法的导航星座在轨重构构型设计[J]. 中国空间科学技术，2018, 38(4): 27-35.
[21] ZHAO Shuang, ZHANG Yasheng, DAI Huayu. Orbit reconstruction configuration of navigation constellation based on differential evolution algorithm[J]. Chinese Space Science and Technology, 2018, 38(4): 27-35.
[22] FAN Q Q, WANG W L, YAN X F. Differential evolution algorithm with strategy adaptation and knowledge-based control parameters[J]. Artificial Intelligence Review, 2019, 51(2): 219-253.
[23] SHENG W X, LIU K Y, LIU Y, et al. Optimal placement and sizing of distributed generation via an improved nondominated sorting genetic algorithm II[J]. IEEE Transactions on Power Delivery, 2015, 30(2): 569-578.
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