空战格斗飞行机动数据库建立及应用

doi:10.7527/S1000-6893.2022.27538

Abstract

Abstract:

The main challenges in autonomous air combat mission include complex environment， strong gaming， fast dynamics， insufficient information， and uncertain boundaries. Nowadays， artificial intelligence （especially human-machine collaborative intelligence） has provided a good opportunity for addressing these challenges. We thus constructed an Air Combat Engagement Database （ACED）. The ACED database is currently focused on Within-Visual-Range （WVR） air combat， which allocates the flight states and controls of experienced human pilots in WVR air combat missions. Based on this database， we first investigate the flight dynamics in air combat missions， and pro-pose that we should primarily analyze the roll angle and vertical load factor for researching the human pilot decision-making. The time window of pilot decision-making in WVR air combat is also studied， and the frequency of pilots maneuvering decision-making is analyzed by energy spectrum method. Moreover， the enemy aircraft trajectory prediction algorithm in WVR air combat is studied， the optimal key parameters of the algorithm are obtained for the trajectory prediction of different types of enemy aircraft. These illustrative applications verify the database established in this paper.

Key words: autonomous air combat, within-visual-range, human-machine collaboration, air combat engagement database, machine learning

CLC Number:

V249

Jinyi MA, Can WANG, Tao XUE, Jianliang AI, Yiqun DONG. Development and illustrative applications of an air combat engagement database[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S1): 727538-727538.

Figures/Tables 10

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References 32

1	BURGIN G H， OWENS A J. An adaptive maneuvering logic computer program for the simulation of one-to-one air-to-air combat. Volume 2： Program description： NASA CR 2583［R］. Washington D. C.： NASA， 1975.
2	BURGIN G H， OWENS A J. An adaptive maneuvering logic computer program for the simulation of one-to-one air-to-air combat. Volume 1： General description： NASA CR 2582［R］. Washington D. C.： NASA， 1975.
3	BURGIN G H， EGGLESTON D M. Design of an all-attitude flight control system to execute commanded bank angles and angles of attack： NASA CR 145004［R］. Washington D. C.： NASA， 1976.
4	HANKINS III WW. Computer-automated opponent for manned air-to-air combat simulations： NASA TP-1518［R］. Washington D. C.： NASA， 1979.
5	GOODRICH K H， MCMANUS J W. Development of a tactical guidance research and evaluation system （TGRES）［C］∥Flight Simulation Technologies Conference and Exhibit. Reston： AIAA， 1989： 3312.
6	GOODRICH K H， MCMANUS J W. An integrated environment for tactical guidance research and evaluation［C］∥Orbital Debris Conference： Technical Issues andFuture Directions. Reston： AIAA， 1990： 1287.
7	GOODRICH K H. A high-fidelity， six-degree-of-freedom batch simulation environment for tactical guidance research and evaluation： NASA TM-4440［R］. Washington D. C.： NASA， 1993.
8	BATTERSON J G， MORELLI E A. Parameter identification flight test maneuvers for closed loop modeling of the F-18 High Alpha Research Vehicle （HARV）： NASACR-198269［R］. Washington D. C.： NASA， 1996.
9	ILIFF K W， WANG K C. Flight-determined subsonic longitu-dinal stability and control derivatives of the F-18 High Angle of Attack Research Vehicle （HARV） with thrust vectoring： NASA/TP-97-206539［R］. Washington D. C.： NASA， 1997.
10	AUSTIN F， CARBONE G， FALCO M， et al. Game theory for automated maneuvering during air-to-air combat［J］. Journal of Guidance， Control， and Dynamics， 1990， 13（6）： 1143-1149.
11	徐光达，吕超，王光辉，等. 基于双矩阵对策的UCAV空战自主机动决策研究［J］. 舰船电子工程， 2017， 37（11）： 24-28， 39.
	XU G D， LV C， WANG G H， et al. Research on UCAV autonomous air combat maneuvering decision-making based on Bi-matrix game［J］. Ship Electronic Engineering， 2017， 37（11）： 24-28， 39 （in Chinese）.
12	MA Y F， MA X L， SONG X. A case study on air combat decision using approximated dynamic programming［J］. Mathematical Problems in Engineering， 2014， 2014： 1-10.
13	MCGREW J S， HOW J P， WILLIAMS B， et al. Air-combat strategy using approximate dynamic programming［J］. Journal of Guidance， Control， and Dynamics， 2010， 33（5）： 1641-1654.
14	VIRTANEN K， RAIVIO T， HAMALAINEN R P. Modeling pilot’s sequential maneuvering decisions by a multistage influence diagram［J］. Journal of Guidance， Control， and Dynamics， 2004， 27（4）： 665-677.
15	VIRTANEN K， KARELAHTI J， RAIVIO T. Modeling air combat by a moving horizon influence diagram game［J］. Journal of Guidance， Control， and Dynamics， 2006， 29（5）： 1080-1091.
16	JOSEPH T. AI claims “Flawless Victory” going undefeated in digital dogfight with human fighter pilot［EB/OL］. ［2023-03-10］. .
17	DONG Y Q， AI J L. Trial input method and own-aircraft state prediction in autonomous air combat［J］. Journal of Aircraft， 2012， 49（3）： 947-954.
18	DONG Y Q， AI J L. Maneuvering strategy and own aircraft movement prediction in trial input method： Low angle of attack［C］∥Infotech@Aerospace 2012. Reston： AIAA， 2012： 2594.
19	DONG Y Q， HUANG J， AI J L. Visual perception-based target aircraft movement prediction for autonomous air combat［J］. Journal of Aircraft， 2014， 52（2）： 538-552.
20	DONG Y Q. Deep learning-based opponent aircraft attitude detection in autonomous air combat［J］. Journal of Aerospace Information Systems， 2019， 16（4）： 162-167.
21	薄涛. 格斗空战行为建模技术研究［D］. 长沙：国防科学技术大学，2002.
	BO T. Research on human behavior representation of fighter dogfight combat［D］. Changsha： College of Mechatronic Engineering and Automation， National University of Defense Technology， 2002 （in Chinese）.
22	徐安，高春庆，寇英信，等. 基于BFM方法的1vs1近距自主空战决策［J］. 系统工程与电子技术， 2020， 42（11）： 2513-2519.
	XU A， GAO C Q， KOU Y X， et al. Autonomous air combat decision of 1vs1 based on BFM method［J］. Systems Engineering and Electronics， 2020， 42（11）： 2513-2519 （in Chinese）.
23	陈斌，王江，王阳. 战斗机嵌入式训练系统中的智能虚拟陪练［J］. 航空学报， 2020， 41（6）： 523467.
	CHEN B， WANG J， WANG Y. Intelligent virtual training partner in embedded training system of fighter［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（6）： 523467 （in Chinese）.
24	董一群，艾剑良. 自主空战技术中的机动决策：进展与展望［J］. 航空学报， 2020， 41（S2）： 724264.
	DONG Y Q， AI J L. Decision making in autonomous air combat： Review and prospects［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（S2）： 724264 （in Chinese）
25	DONG Y Q， AI J L， LIU J Q. Guidance and control for own aircraft in the autonomous air combat： a historical review and future prospects［J］. Proceedings of the Institution of Mechanical Engineers， Part G： Journal of Aerospace Engineering， 2019， 233（16）： 5943-5991.
26	上电所举办首届智能空战竞赛［EB/OL］. ［2023-03-10］. .
	First Intelligent Aerial Combat Competition held by the China Aviation Radio Electronics Research Institute ［EB/OL］. ［2023-03-10］. ."
27	“智慧协同，决战长空” 2021年智能空战竞赛决赛在中国航空无线电电子研究所成功举办［EB/OL］. ［2022-06-01］..
	The 2021 Intelligent Air Combat Competition， Collaboration Wisdom， Battle for the Sky， was successfully held at the China Aeronautical Radio Electronics Research Institute ［EB/OL］. ［2022-06-01］. （in Chinese）.
28	DONG Y Q， ZHONG Y X， YU W B， et al. Mcity data collection for automated vehicles study［DB/OL］. preprint arXiv：， 2019.
29	薛涛，马金毅，温炯然，等. 视距内空战飞行机动数据采集及分析［C］∥第40届中国控制会议论文集（15）. 上海：中国自动化学会控制理论专业委员会，中国自动化学会，中国系统工程学会， 2021： 716-721.
	XUE T， MA J Y， WEN J R， et al. Within-visual-range Air Combat Engagement Database of Human Pilots［C］∥Proceedings of the 40th China Control Conference （15）. Shanghai： nical Committee on Control Theory， Chinese Association of Automation， Chinese Association of Automation， Systems Engineering Society of China， 2021： 716-721 （in Chinese）.
30	DONG Y Q. Implementing Deep Learning for comprehensive aircraft icing and actuator/sensor fault detection/identification［J］. Engineering Applications of Artificial Intelligence， 2019， 83： 28-44.
31	DONG Y Q. An application of Deep Neural Networks to the in-flight parameter identification for detection and characterization of aircraft icing［J］. Aerospace Science and Technology， 2018， 77： 34-49.
32	KELKAR S S， GRIGSBY L L， LANGSNER J. An extension of parseval’s theorem and its use in calculating transient energy in the frequency domain［J］. IEEE Transactions on Industrial Electronics， 1983， IE-30（1）： 42-45.

机型	基本构型概述	空机重量/t	机翼载荷/（t·m^-2）	最大速度/（km·h^-1）	最大高度/km	推重比	累计飞行时长/min
F	中型双发舰载三代机	16.9	0.445	2 205	15.0	0.95	340.0
M1	单发三角翼二代机	8.8	0.383	2 237	18.7	0.70	108.8
M2	轻型双发三代机	14.9	0.392	2 400	18.0	1.09	122.4
S	重型双发三代机	24.9	0.402	2 120	17.3	1.00	108.8

多项式阶数	机型	最优采样周期/s	最优历史窗口/s	轨迹平均最大误差/m
1	M1	0.05	0.05	20.231 7
	M2	0.05	0.05	16.325 0
	S	0.05	0.05	12.652 0
2	M1	0.05	0.15	9.721 2
	M2	0.05	0.15	8.268 5
	S	0.05	0.10	7.608 5
3	M1	0.05	0.30	7.770 9
	M2	0.05	0.30	7.216 0
	S	0.05	0.30	6.798 8
4	M1	0.10	1.00	8.026 2
	M2	0.10	0.70	7.662 1
	S	0.10	1.10	7.252 8
5	M1	0.20	2.00	9.567 9
	M2	0.10	1.50	9.039 1
	S	0.15	2.10	8.544 1

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Development and illustrative applications of an air combat engagement database

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