EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 42 / No 1 (February 2024) JNWPU, 42 1 (2024) 117-128 References
Open Access
Issue
JNWPU
Volume 42, Number 1, February 2024
Page(s) 117 - 128
DOI https://doi.org/10.1051/jnwpu/20244210117
Published online 29 March 2024
  1. RUMFORD T E. Demonstration of autonomous rendezvous technology(dart) project summary[J]. Space Systems Technology and Dperations, 2003, 5088: 10–19. [Article] [CrossRef] [Google Scholar]
  2. WEISMULLER T, LEINZ M. GNC demonstrated by the orbital express autonomous rendezvous and capture sensor system[C]//Proceedings of the 29th Annual AAS Guidance and Control Conference, 2006 [Google Scholar]
  3. LI Zhenyu, ZHU Hai. Survey on spacecraft orbital pursuit-evasion dferental games[J]. Scientia Sinica Technologica, 2020, 50(12): 1533–1545. [Article] (in Chinese) [CrossRef] [Google Scholar]
  4. YU Dateng, WANG Hua, ZHOU Wanmeng. Anti-rendezvous evasive maneuver method considering space geometrical relationship[J]. Journal of National University of Defense Technology, 2016, 38(6): 89–94. [Article] (in Chinese) [Google Scholar]
  5. QIAN Xingfang, LIN Ruixiong, ZHAO Yanan. Missile flight mechanics[M]. Beijing: Beijing Institute of Technology Press, 2006 (in Chinese) [Google Scholar]
  6. LI Chaoyong. Study on guidance and control problems for tactical ballistic missile interceptor[D]. Harbin: Harbin Institute of Technology, 2008 (in Chinese) [Google Scholar]
  7. ISAACS R. Differential Games[M]. New York: John Wiley and Sons, 1965 [Google Scholar]
  8. INNOCENTI M A, TARIAGIA V. Game tharec stalegies for spaceraif endezvous and mofion synchronzalion[C]//AIAA Guidance, Navigation and Control Conference, 2016 [Google Scholar]
  9. BARCHAN R, GCHOSE D. An SDRE based difrenial game approach for maneuvering target in erception[C]//AIAA Guidance, Navigation, and Control Conference, 2015 [Google Scholar]
  10. LI Z Y, ZHU H, YANG Z. A dimension-eduction solution of fre-time diferential games for spacecraft ursuit-evasion[J]. Acta Astronautica, 2019, 163: 201–210. [Article] [CrossRef] [Google Scholar]
  11. LIU Yanfang. Research on end-game guidance law for interceptor missile based on differential game theory[D]. Harbin: Harbin Institute of Technology, 2014 (in Chinese) [Google Scholar]
  12. ZHU Q, SHAO Z J. Missile real-time receding horizon pursuit and evasion games guidance based on neural network[J]. Systems Engineering and Electronics, 2019(7): 1597–1605 [Google Scholar]
  13. CAO Lei. Key technologies of intelligent game confrontation based on deep reinforcement learning[J]. Command Information System and Technology, 2019, 10(5): 1–7. [Article] (in Chinese) [Google Scholar]
  14. CHUN X, ALFRIEND K T, ZHANG J, et al. Q-learning algorithm for pathplanning to maneuver through a satellite cluster[C]//AAS/AIAA Astro-Dynamics Specialist Conference, 2018: 218–268 [Google Scholar]
  15. LIU Bingyan, YE Xiongbing, GAO Yong, et al. Strategy solution of norn-cooperative target pursuit evasion game based on branching deep rein-forcement learning[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10): 348–358. [Article] (in Chinese) [Google Scholar]
  16. WU Qichang. Autonomous planning of spacecraft pursuit-evasion maneuver trajectory based on artificial intelligence method[D]. Changsha: National University of Defense Technology, 2019 (in Chinese) [Google Scholar]
  17. WU Qichang, ZHANG Hongbo. Spacecraft pursuit strategy and numerical solution based on survival differential strategy[J]. Control and Information Technology, 2019(4): 39–43. [Article] (in Chinese) [Google Scholar]
  18. LIAO Tian. Research on control and solving method of pursuit-evasion game for spacecraft[D]. Harbin: Harbin Institute of Technology, 2021 (in Chinese) [Google Scholar]
  19. ANDRYCHOWICZ M, WOLSKI F, RAY A, et al. Hindsight experience replay[C]//30th Annual Conference on Neural Information Processing Systems, 2017 [Google Scholar]
  20. MANIA H, GUY A, RECHT B. Simple random search of static linear policies is competitive for reinforcement learning[C]//31st Annual Conference on Neural Information Processing Systems, 2018 [Google Scholar]
  21. CONTI E, MADHAVAN V, SUCH F P, et al. Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents[C]//31st Annual Conference on Neural Information Processing Systems, 2018 [Google Scholar]
  22. GU Y, CHENG Y, CHEN C L P, et al. Proximal policy optimization with policy feedback[J]. IEEE Trans on Systems, Man, and Cybernetics: Systems, 2021, 52(7): 4600–4610 [Google Scholar]
  23. SILVER D, LEVER G, HEESS N, et al. Deterministic policy gradient algorithms[C]//International Conference on Machine Learning, 2014 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.