Manual-Based Automated Maneuvering Decisions for Air-to-Air Combat

IF 1.3 4区 工程技术 Q2 ENGINEERING, AEROSPACE
Kwangjin Yang, Songhyon Kim, Younggun Lee, Changyoung Jang, Yong-Duk Kim
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引用次数: 0

Abstract

A novel new air combat algorithm is proposed, which is based on the knowledge extracted from the experience of human pilots. First, to implement a fighter that maneuvers based on manual control, the maneuver form of the fighter is analyzed and represented as a block. Second, the blocks for each function are connected based on their relationship, and a flow diagram is presented according to the engagement situation of the adversary and ownship. Third, a behavior tree model is applied as a decision-making model to implement the flow diagram as a simulation program. The behavior tree offers good scalability because nonleaf nodes can be added when sophisticated and complex decision-making is required. The proposed method has the advantage of making all maneuvers performed by the algorithm understandable and interpretable. Additionally, it can replace expensive and dangerous dogfighting training for student pilots because the proposed model can emulate maneuvers that manned pilots would perform. To verify the proposed method, the evaluation criteria from the AlphaDogfight Trials are equally applied in the simulation. The experimental results demonstrate that the proposed method has superior engagement capability as compared to the existing air-to-air combat models.
空对空作战中基于手动的自动机动决策
提出了一种基于人类飞行员经验知识提取的空战算法。首先,为了实现基于人工控制的战斗机机动,对战斗机的机动形式进行了分析,并将其表示为块。其次,根据每个功能的块之间的关系将它们连接起来,并根据对手的交战情况和所有权给出流程图。第三,采用行为树模型作为决策模型,实现流程图作为仿真程序。行为树提供了良好的可伸缩性,因为当需要复杂的决策时可以添加非叶节点。该方法的优点是使算法执行的所有操作都是可理解和可解释的。此外,它可以取代昂贵和危险的斗狗训练学员飞行员,因为提出的模型可以模拟机动,有人驾驶的飞行员将执行。为了验证所提出的方法,将AlphaDogfight试验中的评价标准同样应用于仿真。实验结果表明,与现有的空对空作战模型相比,该方法具有优越的作战能力。
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来源期刊
CiteScore
3.70
自引率
13.30%
发文量
58
审稿时长
>12 weeks
期刊介绍: This Journal is devoted to the dissemination of original archival research papers describing new theoretical developments, novel applications, and case studies regarding advances in aerospace computing, information, and networks and communication systems that address aerospace-specific issues. Issues related to signal processing, electromagnetics, antenna theory, and the basic networking hardware transmission technologies of a network are not within the scope of this journal. Topics include aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. The Journal also features Technical Notes that discuss particular technical innovations or applications in the topics described above. Papers are also sought that rigorously review the results of recent research developments. In addition to original research papers and reviews, the journal publishes articles that review books, conferences, social media, and new educational modes applicable to the scope of the Journal.
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