State estimation method for spacecraft autonomous navigation: Review

被引：0
|
作者：
Wang D. [1 ]
Hou B. [1 ,2 ]
Wang J. [2 ]
Ge D. [1 ]
Li M. [3 ]
Xu C. [3 ]
Zhou H. [2 ]
机构：
[1] Beijing Institute of Spacecraft System Engineering, Beijing
[2] College of Liberal Arts and Sciences, National University of Defense Technology, Changsha
[3] Beijing Institute of Control Engineering, Beijing
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2021年 / 42卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Autonomous navigation; Navigation filter algorithms; Observability analysis; Spacecraft; State estimation; System error compensation;
D O I：
10.7527/S1000-6893.2020.24310
中图分类号：
学科分类号：
摘要：
Autonomous navigation is the key technology of spacecraft autonomous operation, while state estimation, the core means of spacecraft autonomous navigation which refers to the real-time determination of spacecraft orbit position, velocity, attitude and other navigation parameters, is one of the key development directions of the spacecraft autonomous navigation technology. Aiming at the practical requirements of the spacecraft autonomous navigation, this paper illustrates the necessity of studying spacecraft autonomous navigation state estimation method and introduces its research status from three aspects, including observability analysis of the navigation system, the navigation filtering algorithm, and error compensation of the navigation system. Then practical applications of the state estimation method in the spacecraft autonomous navigation system is analyzed and summarized. Finally, based on theoretical research and practical applications, the main problems of the state estimation method are presented and the future developments are prospected. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.
引用
收藏
相关论文
共 132 条
  • [1] YANG B H., Spacecraft guidance, navigation and control, (2011)
  • [2] WANG D Y, WEI C L, XIONG K., Autonomous spacecraft navigation technology, (2017)
  • [3] TRUSZKOWSKI W, HALLOCK H, ROUFF C, Et al., Autonomous and autonomic systems: With applications to NASA intelligent spacecraft operations and exploration systems, pp. 1-4, (2009)
  • [4] CHORY M, HOFFMAN D, LEMAY J., Satellite autonomous navigation-status and history, PLANS'86-Position Location and Navigation Symposium, pp. 110-121, (1986)
  • [5] BHATIA R, GELLER D K., RAON: Revolution in Autonomous Orbital Navigation, AAS Guidance and Control Conference, pp. 18-81, (2018)
  • [6] KOZOREZ D A, KRUZHKOV D M., Autonomous navigation of the space debris collector, INCAS Bulletin, 11, pp. 105-113, (2019)
  • [7] GREWAL M S, ANDREWS A P., Applications of Kalman filtering in aerospace 1960 to the present historical perspectives, IEEE Control Systems Magazine, 30, 3, pp. 69-78, (2010)
  • [8] QIN Y Y, ZHANG H Y, WANG S H., Kalman filtering and integrated navigation principles, pp. 3-4, (2012)
  • [9] WANG D Y, LI M D, HUANG X Y, Et al., Spacecraft autonomous navigation technology based on multi-source information fusion, pp. 1-8, (2018)
  • [10] KALMAN R E., Contributions to the theory of optimal control, Computer Science, Mathematics, 5, 2, pp. 102-119, (1960)
12345678910