航空学报 > 2020, Vol. 41 Issue (S2): 724264-724264   doi: 10.7527/S1000-6893.2020.24264

自主空战技术中的机动决策:进展与展望

董一群, 艾剑良   

  1. 复旦大学 航空航天系, 上海 200433
  • 收稿日期:2020-05-22 修回日期:2020-06-01 发布日期:2020-06-24
  • 通讯作者: 艾剑良 E-mail:aijl@fudan.edu.cn

Decision making in autonomous air combat: Review and prospects

DONG Yiqun, AI Jianliang   

  1. Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433
  • Received:2020-05-22 Revised:2020-06-01 Published:2020-06-24

摘要: 自主空战(AAC)是指飞机依靠机载等相关设备,自主进行战场感知、决策及控制,以执飞空战的技术,其核心是机动决策模块。针对该模块的研究,调研了国内外的现有方法,并按照有关方法的核心内涵,将其分为基于数学求解、机器搜索、以及数据驱动3类分别简要概述;针对每一类方法列出了具有代表性的示例技术,并讨论了其优缺点。指出了自主空战决策的研究应立足现有相关方法及技术基础,同时积极吸纳机器学习、人工智能等新兴技术,合理利用各类方法的优点,取长补短,实现自主空战决策的长足发展;有关于该研究设想的关键科学问题及其潜在解决方案也在文中进行了初步讨论。希冀基于针对自主空战决策研究框架及实现方法的概念性讨论,促进其技术发展,推广相关思路在有关国家安全、国民经济建设等领域的应用,以满足国家重大需求和工程实践。

关键词: 自主空战, 机动决策, 机器学习, 人工智能, 机器搜索

Abstract: In the Autonomous Air Combat (AAC) technique, the aircraft is expected to autonomously perform situational perception, decision making, and control execution in the combat, among which decision making is the core of the AAC technique. This paper reviews the state of the art decision-making methods in the AAC technique by dividing them into three groups, i.e. mathematics-based, knowledge-encoded, and learning-driven methods. We list the representative techniques in each group, discussing both the weaknesses and strengths. We point out that the future development of AAC should root in the traditional mathematical approaches, while also incorporating novel techniques, e.g. machine learning and artificial intelligence. Both challenges and potential solutions to this proposal are listed. This paper delivers a brief analysis of past experiences and future prospects of the AAC development, hoping to promote the academic research and engineering applications.

Key words: autonomous air combat (AAC), decision making, machine learning, artificial intelligence, machine search

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