用于视距内空战的可解释基本战斗机操纵决策支持方案

IF 1.3 4区 工程技术 Q2 ENGINEERING, AEROSPACE Journal of Aerospace Information Systems Pub Date : 2024-04-17 DOI:10.2514/1.i011388
Can Wang, Jingqi Tu, Xizhong Yang, Jun Yao, Tao Xue, Jinyi Ma, Yiming Zhang, Jianliang Ai, Yiqun Dong
{"title":"用于视距内空战的可解释基本战斗机操纵决策支持方案","authors":"Can Wang, Jingqi Tu, Xizhong Yang, Jun Yao, Tao Xue, Jinyi Ma, Yiming Zhang, Jianliang Ai, Yiqun Dong","doi":"10.2514/1.i011388","DOIUrl":null,"url":null,"abstract":"<p>In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. A BFM decision support scheme has been proposed to aid human pilots in the complex air combat engagement. Recent artificial intelligence advances provide novel opportunities for the development of BFM decision support research. This paper commences by establishing an air-combat-engagement database. Key features that pilots rely on for BFM decision-making in WVR air combat are analyzed, which identifies the input and output data essential for the development of the BFM decision support scheme. A Long Short-Term-Memory (LSTM)-based BFM decision support scheme is then proposed to map input (i.e., combat situations) to output (i.e., BFM decision). Additionally, Shapley-Additive-Explanations-based explainability analysis is also employed to assess the importance of each input feature in the LSTM blocks, and to explain the contribution of each feature to the BFM decision. To evaluate the effectiveness of the proposed BFM decision support scheme, WVR air-combat tests are conducted, which justify the effectiveness of the proposed scheme.</p>","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"43 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Explainable Basic-Fighter-Maneuver Decision Support Scheme for Piloting Within-Visual-Range Air Combat\",\"authors\":\"Can Wang, Jingqi Tu, Xizhong Yang, Jun Yao, Tao Xue, Jinyi Ma, Yiming Zhang, Jianliang Ai, Yiqun Dong\",\"doi\":\"10.2514/1.i011388\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. A BFM decision support scheme has been proposed to aid human pilots in the complex air combat engagement. Recent artificial intelligence advances provide novel opportunities for the development of BFM decision support research. This paper commences by establishing an air-combat-engagement database. Key features that pilots rely on for BFM decision-making in WVR air combat are analyzed, which identifies the input and output data essential for the development of the BFM decision support scheme. A Long Short-Term-Memory (LSTM)-based BFM decision support scheme is then proposed to map input (i.e., combat situations) to output (i.e., BFM decision). Additionally, Shapley-Additive-Explanations-based explainability analysis is also employed to assess the importance of each input feature in the LSTM blocks, and to explain the contribution of each feature to the BFM decision. To evaluate the effectiveness of the proposed BFM decision support scheme, WVR air-combat tests are conducted, which justify the effectiveness of the proposed scheme.</p>\",\"PeriodicalId\":50260,\"journal\":{\"name\":\"Journal of Aerospace Information Systems\",\"volume\":\"43 1\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Aerospace Information Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.2514/1.i011388\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Aerospace Information Systems","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2514/1.i011388","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0

摘要

在视距内(WVR)空战中,战斗机基本机动(BFM)被广泛使用。有人提出了一种 BFM 决策支持方案,以帮助人类飞行员应对复杂的空战。最近人工智能的进步为 BFM 决策支持研究的发展提供了新的机遇。本文首先建立了一个空战交战数据库。分析了飞行员在 WVR 空战中进行 BFM 决策所依赖的关键特征,从而确定了开发 BFM 决策支持方案所必需的输入和输出数据。然后提出了一种基于长短期记忆(LSTM)的 BFM 决策支持方案,以将输入(即战斗情况)映射到输出(即 BFM 决策)。此外,还采用了基于 Shapley-Additive-Explanations 的可解释性分析来评估 LSTM 模块中每个输入特征的重要性,并解释每个特征对 BFM 决策的贡献。为了评估所提出的 BFM 决策支持方案的有效性,我们进行了 WVR 空战测试,证明了所提出方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Explainable Basic-Fighter-Maneuver Decision Support Scheme for Piloting Within-Visual-Range Air Combat

In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. A BFM decision support scheme has been proposed to aid human pilots in the complex air combat engagement. Recent artificial intelligence advances provide novel opportunities for the development of BFM decision support research. This paper commences by establishing an air-combat-engagement database. Key features that pilots rely on for BFM decision-making in WVR air combat are analyzed, which identifies the input and output data essential for the development of the BFM decision support scheme. A Long Short-Term-Memory (LSTM)-based BFM decision support scheme is then proposed to map input (i.e., combat situations) to output (i.e., BFM decision). Additionally, Shapley-Additive-Explanations-based explainability analysis is also employed to assess the importance of each input feature in the LSTM blocks, and to explain the contribution of each feature to the BFM decision. To evaluate the effectiveness of the proposed BFM decision support scheme, WVR air-combat tests are conducted, which justify the effectiveness of the proposed scheme.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
New Type-2-Fuzzy-Logic-Based Control System for the Cessna Citation X Basic Engagement Zones Advanced Wavelet Transform-Based Automated System for Drone State Identification Using Radio-Frequency Signal Integration of the Functional Hazard Assessment Within a Model-Based Systems Engineering Framework Safe Spacecraft Inspection via Deep Reinforcement Learning and Discrete Control Barrier Functions
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1