Research on the evaluation of dynamic decision-making effectiveness of UAV’s air combat

doi:10.23919/JSEE.2026.000060

Journal of Systems Engineering and Electronics ›› 2026, Vol. 37 ›› Issue (2): 534-547.doi: 10.23919/JSEE.2026.000060

• SYSTEMS ENGINEERING • Previous Articles

Research on the evaluation of dynamic decision-making effectiveness of UAV’s air combat

Shulin DING¹^,²(), Yuhui WANG¹^,*(), Haodi ZHANG¹()

¹College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
²Beijing Institute of Mechanical Equipment, Beijing 100854, China

Received:2024-03-27 Online:2026-04-18 Published:2026-04-30
Contact: Yuhui WANG E-mail:dingsl@nuaa.edu.cn;wangyh@nuaa.edu.cn;zhanghd@nuaa.edu.cn
About author:
DING Shulin was born in 1999. She received her B.S. degree in electrical engineering and its automation from University of Jinan, Jinan, China, in 2021. She is currently pushing her master’s degree in digital information, with Nanjing University of Aeronautics and Astronautics. Her research interest is effectiveness evaluation of air combat decision-making. E-mail: dingsl@nuaa.edu.cn

WANG Yuhui was born in 1980. She received her B.S. degree in electrical engineering and its automation from University of Jinan, Jinan, China, in 2001, and Ph.D. degree in control theory and control engineering from Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 2008. She is currently a full professor with the College of Automation Engineering, NUAA. Her current research interests include nonlinear system control, intelligent control, and flight control. E-mail: wangyh@nuaa.edu.cn

ZHANG Haodi was born in 1999. He received his B.S. degree in detection guidance and control technology from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2021. He is currently pushing his master’s degree in control science and engineering, with Nanjing University of Aeronautics and Astronautics. His research interest is integrated fire control. E-mail: zhanghd@nuaa.edu.cn
Supported by:
This work was supported by the Major Projects for Science and Technology Innovation 2030 (2018AAA0100805) and National Natural Science Foundation of China (62373187).

Abstract

Abstract:

The evaluation of air combat decision-making has garnered significant attention due to its potential to effectively mitigate losses resulting from erroneous decisions. However, existing research primarily focuses on static evaluation methods. Therefore, this paper proposes a dynamic multi-round decision evaluation method based on the characteristics of multi-round unmanned aerial vehicle air combat under opponent’s optimal strategy. In order to determine objective weights, an improved multi-attribute decision making method is proposed, which incorporates the proximity as a correction coefficient for evaluation indicators, utilizing the cosine similarity instead of Euclidean distance, and incorporating both actual and theoretical objective weights to prevent data mutations. Subsequently, the game theory is employed to reasonably adjust subjective and objective weights to obtain comprehensive weights. To address the issues related to the ambiguity and randomness during the evaluation process, a reverse cloud generator is utilized to determine the center of gravity of the cloud model using comprehensive weights while employing the weighted deviation degree for evaluating air combat decision-making effectiveness. By activating the cloud generator through the cloud model, the optimal strategies for each round of air combat are determined, thereby completing the dynamic evaluations for multi-round sequential decision-making processes. Finally, the feasibility and effectiveness of the proposed method are verified through simulations.

Key words: dynamic decision-making effectiveness evaluation, improved multi-attribute decision making, game theory, cloud model

Shulin DING, Yuhui WANG, Haodi ZHANG. Research on the evaluation of dynamic decision-making effectiveness of UAV’s air combat[J]. Journal of Systems Engineering and Electronics, 2026, 37(2): 534-547.

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References 30

1	LI S Y, CHEN M, WANG Y H, et al Air combat decision-making of multiple UCAVs based on constraint strategy games. Defence Technology, 2022, 18 (3): 368- 383. doi: 10.1016/j.dt.2021.01.005
2	ZHOU W Q, ZHU J H, KUANG M C, et al Multi-UAV cooperative swarm algorithm in air combat based on predictive game tree. SCIENTIA SINICA Technologica, 2023, 53 (2): 187- 199. doi: 10.1360/SST-2021-0294
3	FAN J R, LI D G, LI R P, et al Analysis on MAV/UAV cooperative combat based on complex network. Defence Technology, 2020, 16 (1): 150- 157. doi: 10.1016/j.dt.2019.09.002
4	BAYKASOGLU A, GOLCUK I A dynamic multiple attribute decision making model with learning of fuzzy cognitive maps. Computers & Industrial Engineering, 2019, 135, 1063- 1076. doi: 10.1016/j.cie.2019.06.032
5	ZAYAT W, KILIC H S, YALCIN A S, et al. Application of MADM methods in industry 4.0: a literature review. Computers & Industrial Engineering, 2023, 177: 109075.
6	JANA C, PAL M A dynamical hybrid method to design decision making process based on GRA approach for multiple attributes problem. Engineering Applications of Artificial Intelligence, 2021, 100, 104203. doi: 10.1016/j.engappai.2021.104203
7	CHEN S W, LI Z G, XU Q S Grey target theory based equipment condition monitoring and wear mode recognition. Wear, 2006, 260 (4/5): 438- 449. doi: 10.1016/j.wear.2005.02.085
8	YOON K, HWANG C L. Multiple attribute decision making: methods and applications. Berlin: Springer-Verlag, 1981.
9	HU H J, LIAO Y L Application of Bayesian network and genetic algorithm in construction safety evaluation. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 2024, 177 (3): 199- 208. doi: 10.1680/jsmic.22.00034
10	ZHOU J X, WEI S B, CHAI Y Using improved dynamic Bayesian networks in reliability evaluation for flexible test system of aerospace pyromechanical device products. Reliability Engineering & System Safety, 2021, 210, 107508. doi: 10.1016/j.ress.2021.107508
11	XING Z K, HE Y G, CHEN J F, et al Health evaluation of power transformer using deep learning neural network. Electric Power Systems Research, 2023, 215, 109016. doi: 10.1016/j.jpgr.2022.109016
12	SUN W, XU Y F Financial security evaluation of the electric power industry in China based on a back propagation neural network optimized by genetic algorithm. Energy, 2016, 101, 366- 379. doi: 10.1016/j.energy.2016.02.046
13	SUN Y K, FANG Z G, CHEN D Multi temporal threat assessment based on dynamic grey principal component analysis. Systems Engineering and Electronics, 2021, 43 (3): 740- 746.
14	SU L. Research on the efficiency evaluation model and method of airborne fire control radar under multi-machine collaborative conditions. Zhengzhou: Zhengzhou University, 2016. (in Chinese)
15	SHIDPOUR H, CUNHA C D, BERNARD A Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory. Expert Systems with Applications, 2016, 64, 633- 644. doi: 10.1016/j.eswa.2016.08.022
16	HE Y X, PANG Y X, ZHANG Q, et al Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory. Renewable Energy, 2018, 122, 643- 653. doi: 10.1016/j.renene.2018.02.028
17	WU H W, ZHEN J, ZHANG J Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model. Journal of Rail Transport Planning & Management, 2020, 16, 100206- 100221. doi: 10.1016/j.jrtpm.2020.100206
18	SHI L K, PEI Y, YUN Q J, et al Agent-based effectiveness evaluation method and impact analysis of airborne laser weapon system in cooperation combat. Chinese Journal of Aeronautics, 2023, 36 (4): 442- 454. doi: 10.1016/j.cja.2022.11.006
19	MA S D, ZHANG H Z, YANG G Q Target threat level assessment based on cloud model under fuzzy and uncertain conditions in air combat simulation. Aerospace Science and Technology, 2017, 67, 49- 53. doi: 10.1016/j.ast.2017.03.033
20	XU A, CHEN X, LI Z W, et al A situation assessment method for beyond visual range air combat based on tactical attack zone. Firepower and Command and Control, 2020, 45 (9): 97- 102.
21	GAO Y Y, YU M J Multi-target threat level assessment in air combat based on cloud model and improved entropy weight. Firepower and Command and Control, 2018, 43 (9): 35- 39.
22	ZHANG H J, LU M K, KE X B, et al Evaluation model of black-start schemes based on optimal combination weights and improved VIKOR method. International Journal of Electrical Power & Energy Systems, 2021, 129, 106762. doi: 10.1016/j.ijepes.2021.106762
23	MARAVER D, SIN A, SEBASTIAN F, et al Environmental assessment of CCHP (combined cooling heating and power) systems based on biomass combustion incomparison to conventional generation. Energy, 2013, 57 (8): 17- 23. doi: 10.1016/j.energy.2013.02.014
24	LI H. Research on threat assessment methods for air raid targets in air defense operations. Xi’an: Xi’an University of Technology, 2023. (in Chinese)
25	DING J F, SI G Y, MA J, et al Mission evaluation: expert evaluation system for large-scale combat tasks of the weapon system of systems. SCIENCE CHINA Information Sciences, 2018, 61 (1): 1- 19. doi: 10.1007/s11432-016-9071-5
26	HUANG L, ZHU J, DUAN Q F Extensive game decision based on the PPO-CFR algorithm under incomplete information. SCIENTIA SINICA Information Sciences, 2022, 52 (12): 2178.
27	LI D Y, LIU C Y On the universality of normal cloud model. Engineering Science of China, 2004, (8): 28- 34.
28	ZHU J H, YANG Q, JIANG J Identifying crucial deficiency categories influencing ship detention: a method of combining cloud model and prospect theory. Reliability Engineering & System Safety, 2023, 230, 108949. doi: 10.1016/j.ress.2022.108949
29	RUAN D M, BIAN J M, WANG Q, et al Application of modified cloud model-level eigenvalue method in water quality evaluation. Journal of Hydrology, 2021, 603, 606980.
30	WEN X X, NIE Y, DU Z X, et al Operational safety assessment of straddle-type monorail vehicle system based on cloud model and improved CRITIC method. Engineering Failure Analysis, 2022, 139, 106463. doi: 10.1016/j.engfailanal.2022.106463

R/B	$ x/{\mathrm{km}} $	$ y/{\mathrm{km}} $	$ z/{\mathrm{km}} $	$ v/{\mathrm{(m/s)}} $	$ \gamma /(^\circ) $	$ \psi /(^\circ) $	Performance
$ R1 $	5	5	4.95	125	5	30	0.6
$ R2 $	5.5	5	4.9	125	7.5	45	0.6
$ B1 $	50	52	4.85	100	15	−120	0.4
$ B2 $	50	55	4.75	100	7.5	−150	0.4
$ B3 $	50	60	4.75	100	7.5	−120	0.4
$ B4 $	48	55	4.85	100	7.5	−77	0.4

Method	Evaluation time/s
Virtual cloud evaluation method without considering opponent’s strategies	231.357
Cloud evaluation method without considering opponent’s strategies	128.166
Virtual cloud evaluation method under opponent’s optimal strategy	100.243
Cloud evaluation method under opponent’s optimal strategy	2.448

Round	$ R1 $	$ R2 $	Round	$ R1 $	$ R2 $
1	Acceleration flight	Climb	10	Yaw left	Acceleration flight
2	Acceleration flight	Yaw right	11	Yaw left	Dive
3	Acceleration flight	Acceleration flight	12	Yaw right	Acceleration flight
4	Acceleration flight	Acceleration flight	13	Yaw right	Acceleration flight
5	Acceleration flight	Acceleration flight	14	Yaw right	Acceleration flight
6	Acceleration flight	Acceleration flight	15	Acceleration flight	Dive
7	Acceleration flight	Acceleration flight	16	Acceleration flight	Dive
8	Acceleration flight	Acceleration flight	17	Uniformity flight	Acceleration flight
9	Acceleration flight	Acceleration flight	18	Climb	Dive

Method	Subjective weights of situational indicators	Manual modification of the judgment matrix	Number of computations
AHP	$ \begin{aligned}&(0.410,0.348,\\&0.180,0.061)\end{aligned} $	Yes	Greater than or equal to twice
IAHP	$ \begin{aligned}&(0.382,0.365,\\&0.185,0.068)\end{aligned} $	No	Once

R/B	$ x/{\mathrm{km}} $	$ y/{\mathrm{km}} $	$ z/{\mathrm{km}} $	$ v/{\mathrm{(m/s)}} $	$ \gamma /(^\circ) $	$ \psi /(^\circ) $	Performance
$ R1 $	21	40	4.9	125	5	30	0.6
$ R2 $	25	45	5.4	125	7.5	45	0.6
$ B1 $	80	75	4.75	100	15	−120	0.4
$ B2 $	80	70	4.85	100	15	−150	0.4
$ B3 $	80	80	4.85	100	7.5	−120	0.4
$ B4 $	78	80	4.75	100	7.5	−77	0.4

Research on the evaluation of dynamic decision-making effectiveness of UAV’s air combat

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