基于隐马尔可夫模型的空战决策点理论研究

冯超; 景小宁; 李秋妮; 姚鹏

doi:10.13700/j.bh.1001-5965.2016.0220

基于隐马尔可夫模型的空战决策点理论研究

doi: 10.13700/j.bh.1001-5965.2016.0220

空军工程大学航空航天工程学院, 西安 710038

基金项目:

国家杰出青年科学基金 71501184

航空科学基金 20155196022

详细信息

作者简介:
冯超, 男, 硕士研究生。主要研究方向:航空兵任务规划与训练效能评估

景小宁, 女, 博士, 副教授。主要研究方向:航空兵任务规划

李秋妮, 女, 硕士, 讲师。主要研究方向:智能算法、博弈决策与协同控制

姚鹏, 男, 硕士研究生。主要研究方向:航空兵任务规划

通讯作者:
景小宁, E-mail:1216682261@qq.com

中图分类号: V323
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出版历程
- 收稿日期: 2016-03-21
- 录用日期: 2016-09-09
- 网络出版日期: 2017-03-20

Theoretical research of decision-making point in air combat based on hidden Markov model

College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an 710038, China

Funds:

National Science Fund for Distinguished Young Scholars 71501184

Aeronautical Science Foundation of China 20155196022

More Information

Corresponding author: JING Xiaoning, E-mail:1216682261@qq.com

摘要

摘要:
从几何空战理论经过能量空战理论，直到角度空战理论，空战理论的发展更多的是从战斗机性能的角度来分析空战过程，忽略了作战飞行员在决策过程中起到的作用。本文分析空战飞机的客观数据的变化特征，提出了一种基于隐马尔可夫的近距空战流程分析方法，使用维特比算法判断飞行员在空战过程中的状态序列，从而获得了理论上的空战决策点。在理论分析上，提出了一种空战决策点理论用以评判飞行员飞行品质。通过实验仿真验证了使用隐马尔可夫模型讨论近距空战的可行性，并且发现飞行员空战决策点包络处于包围趋势时，飞行员获胜的可能性越大。
- 近距空战评估 /
- OODA环 /
- 隐马尔可夫模型 /
- 维特比算法 /
- 动态规划
Abstract:
From geometric air combat to energy air combat, till angle air combat theory, the process of air combat is analyzed more from the fighter performance point of view, the effect of the operational pilot in decision-making is neglected. This paper analyzes the variation characteristics of observation data in air combat, and an analysis method for close-range air combat process based on hidden Markov model is proposed. Viterbi algorithm is used to judge the pilot state sequence in air combat, and then the decision-making point is acquired in theory. Through theoretical analysis, the decision-making point in air combat is proposed to judge the pilot's flight quality. Through simulation, the feasibility of discussion of close-range air combat based on Markov model is verified, and when the pilot's decision-making point is in the tendency of surrounding, the pilot has a higher probability of winning.
- evaluation of close-range air combat /
- OODA loop /
- hidden Markov model /
- Viterbi algorithm /
- dynamic programming

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图 1 角度位置关系示意图

Figure 1. Schematic diagram of relation of angle and position

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图 2 OODA环图

Figure 2. Diagram for OODA loop

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图 3 空战中能量-AGC序列

Figure 3. Energy-AGC sequence in air combat

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图 4 决策点的能量-AGC序列

Figure 4. Energy-AGC sequence for decision-making point

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图 5 包含能量变化过程

Figure 5. Changing process of inherent energy

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图 6 典型的逃逸战术

Figure 6. Typical escape tactical maneuver

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图 7 目标机轨迹图

Figure 7. Trochoid of target plane

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图 8 预先转弯轨迹图

Figure 8. Trochoid of pre turn

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图 9 典型预先转弯仿真图

Figure 9. Simulation diagram of typical "pre turn"

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图 10 对抗双方的关键决策点集合^[11]

Figure 10. Key decision-making set of two sides^[11]

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图 11 空战实例决策点的能量-AGC序列

Figure 11. Energy-AGC sequence for decision-making of two sides in air combat

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图 12 双方包含能量变化过程

Figure 12. Changing process of inherent energy of two sides

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表 1 近距空战双方占位角度关系

Table 1. Relative angular position of two sides in close-range air combat

编号	攻击机位置	目标机位置	ATA/(°)	AOT/(°)	AGC
1			0	0	1.00
2			0	45	0.75
3			0	90	0.75
4			0	180	0.50
5			45	135	0
6			90	90	0
7			90	180	-0.50
8			135	180	-0.75
9			180	180	-1.00

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表 2 飞行员决策点

Table 2. Decision-making point of pilot

编号	目标机状态
1	观察点
2	观察点
3	观察点
4	观察点
5	观察点
6	观察点
7	判断点
8	判断点
9	判断点
10	决策点
11	行动点
12	观察点
13	观察点
14	观察点
15	观察点
16	观察点
17	观察点
18	观察点
19	观察点
20	观察点
21	观察点
22	观察点
23	观察点
24	观察点

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表 3 红方决策点集合

Table 3. Decision-making set of red sides

编号	文献[11]给出的决策点时刻	使用隐马尔可夫模型得到的决策点时刻	对应的判断点时刻	判断过程所用的时间
1	K+18	K+19	K+18	1
2	K+25	K+25	K+24	1
3	K+30	K+30	K+29	1
4	K+34	K+36	K+34	2
5	K+49	K+48	K+47	1
6	K+51	K+52	K+51	1
7	K+57	K+57	K+57	0
8	K+64	K+65	K+64	1
9	K+71	K+71	K+70	1
10	K+75	K+73	K+72	1
11	K+92	K+92	K+91	1
12	K+99	K+98	K+98	0

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表 4 蓝方决策点集合

Table 4. Decision-making set of blue sides

编号	文献[11]给出的决策点时刻	使用隐马尔可夫模型得到的决策点时刻	对应的判断点时刻	判断过程所用的时间
1	K+19	K+19	K+17	2
2	K+45	K+44	K+40	4
3	K+62	K+61	K+59	2
4	K+79	K+79	K+77	2
5	K+107	K+108	K+107	1
6	K+112	K+112	K+111	1

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参考文献(21)

[1]	傅莉, 王晓光.无人战机近距空战微分对策建模研究[J].兵工学报, 2012, 10(10):1210-1216. http://www.cnki.com.cn/Article/CJFDTOTAL-BIGO201210008.htm FU L, WANG X G.Research on close air combat modeling of differential games for unmanned combat air vehicles[J].Acta Armamentarii, 2012, 10(10):1210-1216(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-BIGO201210008.htm
[2]	KRISHNA K K, KANESHIGE J.Artificial immune system approach for air combat maneuvering[C]//Proceedings of SPIE-The International Society for Optical Engineering.Bellingham:SPIE, 2007:274-299.
[3]	ROGER W S, ALAN E B.Neural network models of air combat maneuvering[D].Las Cruces:New Mexico State University, 1992:125-131..
[4]	张立鹏, 魏瑞轩, 李霞.无人作战战斗机空战自主战术决策方法研究[J].电光与控制, 2012, 19(2):92-96. http://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201202024.htm ZHANG L P, WEI R X, LI X.Autonomous tactical decision-making of UCAVs in air combat[J].Electronics Optics & Control, 2012, 19(2):92-96(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201202024.htm
[5]	NUSYIRWAN I F, BIL C.Factorial analysis of a real time optimization for pursuit-evasion problem[C]//Proceedings of the 46th AIAA Aerospace Science Meeting and Exhibit.Reston:AIAA, 2008:195-198.
[6]	杨俊, 谢寿生.基于模糊支持向量机的飞机动作识别[J].航空学报, 2005, 26(6):738-742. http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB200506016.htm YANG J, XIE S S.Fuzzy support vector machines based recognition for aeroplane flight action[J].Acta Aeronautica et Astronautica Sinica, 2005, 26(6):738-742(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB200506016.htm
[7]	KAI V, JANNE K, TUOMAS R.Modeling air combat by a moving horizon influence diagram game[J].Journal of Guidance, Control, and Dynamics, 2006, 29(5):1080-1091.
[8]	钟友武, 柳嘉润, 申功璋.自主近距空战中敌机的战术动作识别方法[J].北京航空航天大学学报, 2007, 33(9):1056-1059. ZHONG Y W, LIU J R, SHEN G Z.Recognition method for tactical maneuver of target in autonomous close-in air combat[J].Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(9):1056-1059(in Chinese).
[9]	张涛, 于雷, 周中良, 等.基于混合算法的空战机动决策[J].系统工程与电子技术, 2013, 35(7):1445-1450. http://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201307017.htm ZHANG T, YU L, ZHOU Z L, et al.Decision-making for air combat maneuvering based on hybrid algorithm[J].Systems Engineering and Electronics, 2013, 35(7):1445-1450(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201307017.htm
[10]	刘波, 覃征, 邵利平, 等.基于群集智能的协同多目标攻击空战决策[J].航空学报, 2009, 30(9):1727-1739. http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB200909029.htm LIU B, QIN Z, SHAO L P, et al.Air combat decision making for coordinated multiple target attack using collective intelligence[J].Acta Aeronautica et Astronautica Sinica, 2009, 30(9):1727-1739(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB200909029.htm
[11]	左家亮, 杨任农, 张滢.基于模糊聚类的近距空战决策过程重构与评估[J].航空学报, 2015, 36(5):1650-1660. http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201505028.htm ZUO J L, YANG R N, ZHANG Y, et al.Reconstruction and evaluation of close air combat decision-making process based on fuzzy clustering[J].Acta Aeronautica et Astronautica Sinica, 2015, 36(5):1650-1660(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201505028.htm
[12]	VEERASAMY N.A high-level mapping of cyberter-rorism to the OODA loop[C]//Proceedings of 5th European Conference on Information Management and Evaluation.Red Hook, NY:Curren Associates Inc., 2011:352-360.
[13]	黄建明, 高大鹏.基于OODA环的作战对抗系统动力学模型[J].系统仿真学报, 2012, 24(3):561-574. http://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201203010.htm HUANG J M, GAO D P.Combat systems dynamics model with OODA loop[J].Journal of System Simulation, 2012, 24(3):561-574(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201203010.htm
[14]	RABINER L, JUANG B.An introduction to hidden Markov models[J].IEEE ASSP Magazine, 1986, 28(7):6-10.
[15]	RABINER L.A tutorial on hidden Markov models and selected applications in speech recognition[J].Proceedings of the IEEE, 1989, 77(2):257-286. doi: 10.1109/5.18626
[16]	BAUM L, PETRIE T, SOULES G, et al.A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains[J].Annals of Mathematical Statistics, 1970, 41(3):164-171.
[17]	BILIMES J A.A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models[EB/OL].(1997-06-15)[2016-03-21].
[18]	LARI K, YOUNG S J.Applications of stochastic context-free grammars using the inside-outside algorithm[J].Computer Speech & Language, 1991, 5(3):237-257.
[19]	GHAHRAMANI Z.Learning dynamic Bayesian networks[J].Lecture Notes in Computer Science, 1997, 45(2):168-197.
[20]	RADFORO N, GEOFFREY H, JORDAN M.A view of the EM algorithm that justifies incremental, sparse, and other variants[M]//JORDAN M I.Learning in graphical models.Cambridge, MA:MIT Press, 1999:355-368.
[21]	ROBERT S.Fighter combat:Tactics and maneuvering[M].Annapolis, MD:Naval Institute Press, 1985:84-86.