自主空战技术中的机动决策:进展与展望

doi:10.7527/S1000-6893.2020.24264

Abstract

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

CLC Number:

V249

DONG Yiqun, AI Jianliang. Decision making in autonomous air combat: Review and prospects[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S2): 724264-724264.

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Decision making in autonomous air combat: Review and prospects

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