多域作战下的群目标意图识别与预测

doi:10.12305/j.issn.1001-506X.2022.11.15

Abstract

Abstract:

There are many challenges in multi-domain operations, such as multiple entities, changeable formations and diverse intentions, which make it difficult to comprehensively utilize multiple knowledge. Therefore, manual judgment is mainly used, resulting in a low level of automation. In order to realize the automatic deduction of the situation by the computer, two difficult problems need to be solved: the graphical modeling of knowledge and the synthesis of intention reasoning. Based on the knowledge map of airspace management and control, a multi-domain operational tactical rule base, the mapping relationship between formation and scene situation is built. A multi-entity hierarchical Bayesian network-based group target intent recognition and prediction method is proposed. Firstly, the state and event information of the target entity in the group is used to construct the behavioral reasoning layer of the target combat entity. Secondly, based on the temporal rules, relative distance and heading information of combat entities, a reasoning layer of similar target element intention is constructed. Finally, the general intention reasoning layer of group targets under multi-domain operations is constructed by using the entity sequence collaboration relationship and formation information. Taking the simulation data of aircraft carrier group activities as an example, it is verified that the algorithm proposed in this paper can obtain relatively reliable intention inference results.

Key words: multi-domain operation, group target, tactical rule base, formation, multi-entity hierarchical Bayesian network

CLC Number:

Dianfeng QIAO, Yan LIANG, Chaoxiong MA, Xinyu YANG, Mian WANG, Jianguo LI. Recognition and prediction of group target intention in multi-domain operations[J]. Systems Engineering and Electronics, 2022, 44(11): 3403-3412.

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References 32

1	CURTIS E, LEMAY C. Annex 3-1: Department of the Air Force role in joint all-domain operations (JADO)[R]. Alabama: Curtis E. Lemay Center, 2020: 1-31.
2	HOEHN J R. Joint all-domain command and control (JADC2)[R]. Washington: Congressional Research Service, 2021: 1-25.
3	祝学军, 赵长见, 梁卓, 等. OODA智能赋能技术发展思考[J]. 航空学报, 2021, 42 (4): 524332.
	ZHU X J , ZHAO C J , LIANG Z , et al. Thoughts on technology development of OODA empowered with AI[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42 (4): 524332.
4	陈彩辉, 缐珊珊. 美军"联合全域作战(JADO)"概念浅析[J]. 中国电子科学研究院学报, 2020, 15 (10): 917- 921. doi: 10.3969/j.issn.1673-5692.2020.10.001
	CHEN C H , XIAN S S . An analysis of U.S. Armed Forces "Joint All-Domain Operation" concept[J]. Journal of China Academy of Electronics and Information Technology, 2020, 15 (10): 917- 921. doi: 10.3969/j.issn.1673-5692.2020.10.001
5	PURSER J L . Multi-domain operations and information warfare in the european theater[J]. Military Review, 2020, 100 (6): 58- 65.
6	汤润泽, 张承龙, 李林林, 等. 多武器跨域智能协同对空作战应用及关键技术[J]. 现代防御技术, 2021, 49 (2): 26- 34. doi: 10.3969/j.issn.1009-086x.2021.02.005
	TANG R Z , ZHANG C L , LI L L , et al. Research on applications and key technologies of multi-weapons in cross-domain intelligent coordination air combat[J]. Modern Defence Technology, 2021, 49 (2): 26- 34. doi: 10.3969/j.issn.1009-086x.2021.02.005
7	陶景, 于淼. 联合作战筹划中的态势评估[J]. 国防科技, 2018, 39 (4): 119- 122. doi: 10.13943/j.issn1671-4547.2018.04.24
	TAO J , YU M . A brief discussion on the situation assessment in joint operational planning[J]. Defense Technology Review, 2018, 39 (4): 119- 122. doi: 10.13943/j.issn1671-4547.2018.04.24
8	TRAPSILAWATI F , HERLIANSYAH M K , NUGRAHENI A S A N S , et al. EEG-based analysis of air traffic conflict: investigating controllers' situation awareness, stress level and brain activity during conflict resolution[J]. The Journal of Navigation, 2020, 73 (3): 678- 696. doi: 10.1017/S0373463319000882
9	葛顺, 夏学知. 用于战术意图识别的动态序列贝叶斯网络[J]. 系统工程与电子技术, 2014, 36 (1): 76- 83.
	GE S , XIA X Z . DSBN used for recognition of tactical intention[J]. Systems Engineering and Electronics, 2014, 36 (1): 76- 83.
10	LIGGINS II M HALL D , LINAS J . Handbook of multisensor data fusion: theory and practice[M]. Boca Raton: CRC Press, 2017.
11	熊伟, 何友. 信息融合系统中的态势预测技术[J]. 火力与指挥控制, 2001, 26 (4): 47- 50. doi: 10.3969/j.issn.1002-0640.2001.04.012
	XIONG W , HE Y . Research situation prediction in data fusion system[J]. Fire Control & Command Control, 2001, 26 (4): 47- 50. doi: 10.3969/j.issn.1002-0640.2001.04.012
12	GUO X B , WANG Z , HU W D . Information fusion with Bayesian networks for target recognition[J]. Journal of System Simulation, 2005, 17 (11): 2713- 2716. doi: 10.3969/j.issn.1004-731X.2005.11.037
13	高晓光, 杨宇. 基于贝叶斯网的舰艇防空威胁评估[J]. 战术导弹技术, 2020, (4): 47- 57.47-57, 70
	GAO X G , YANG Y . Threat assessment for warship air defense based on Bayesian network[J]. Tactical Missile Technology, 2020, (4): 47- 57.47-57, 70
14	高晓光, 叶思懋, 邸若海, 等. 基于融合先验方法的贝叶斯网络结构学习[J]. 系统工程与电子技术, 2018, 40 (4): 790- 796.
	GAO X G , YE S M , DI R H , et al. Bayesian network structure learning based on approach using incorporate method[J]. Systems Engineering and Electronics, 2018, 40 (4): 790- 796.
15	HOSSEINI S , IVANOV D . Bayesian networks for supply chain risk, resilience and ripple effect analysis: a literature re view[J]. Expert Systems with Applications, 2020, 161, 113649. doi: 10.1016/j.eswa.2020.113649
16	FAN S , ZHANG J , BLANCO-DAVIS E , et al. Maritime accident prevention strategy formulation from a human factor perspective using Bayesian networks and TOPSIS[J]. Ocean Engineering, 2020, 210, 107544. doi: 10.1016/j.oceaneng.2020.107544
17	XU L X, QIAO D F, LIANG Y, et al. A novel DBN-based intention inference algorithm for warship air combat[C]//Proc. of the IEEE 9th Joint International Information Technology and Artificial Intelligence Conference, 2020: 916-921.
18	周锐, 余舟毅, 池沛, 等. 战术辅助决策系统中的态势评估问题研究[J]. 系统仿真学报, 2005, 17 (9): 2130- 2133.
	ZHOU R , YU Z Y , CHI P , et al. Situation assessment in tactical decision aiding system[J]. Journal of System Simulation, 2005, 17 (9): 2.
19	MCNEILL F M , THRO E . Fuzzy logic: a practical approach[M]. New York: Academic Press, 2014.
20	CAI Y C , ZHANG W M , LIU Z , et al. Situation evaluation and threat assessment model based on group analysis[J]. Fire Control and Command Control, 2008, 33 (2): 36- 40.
21	王晓帆, 王宝树, 罗磊. 基于粗糙集与计划识别的威胁估计方法[J]. 计算机科学, 2011, 38 (S1): 140- 142.
	WANG X F , WANG B S , LUO L . Techniques for thread assessment based on rough set theory and plan recognition[J]. Computer Science, 2011, 38 (S1): 140- 142.
22	LU Q Q , ZHANG X H , GUO H D . A method of situation association using target attribute[J]. Fire Control and Command Control, 2007, 32 (9): 84- 87.
23	LIU J L , HAN Y J , DONG Y J . An effective combination rule of evidence theory in situation assessment[J]. Electronics Optics & Control, 2009, 16 (10): 80- 83.
24	常利伟, 田晓雄, 张宇青, 等. 基于多源异构数据融合的网络安全态势评估体系[J]. 智能系统学报, 2021, 16.
	CHANG L W , TIAN X X , ZHANG Y Q , et al. Network security situation assessment architecture based on multi-source heterogeneous data fusion[J]. CAAI Transactions on Intelligent Systems, 2021, 16 (1): 38- 47.
25	HUANG H X , HE S P , WANG G Q , et al. Research of situation assessment oriented hypothesis based plan recognition[J]. International Journal of Computer Science and Network Security, 2008, 8 (8): 96- 100.
26	YVONNE F, JURGEN B. Defining dynamic Bayesian networks for probabilistic situation assessment[C]//Proc. of the IEEE 15th International Conference on Information Fusion, 2012: 888-895.
27	KAMMOUH O , GARDONI P , CIMELLARO G P . Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks[J]. Reliability Engineering & System Safety, 2020, 198, 106813.
28	ANSARI F , GLAWAR R , SIHN W . Prescriptive maintenance of CPPS by integrating multimodal data with dynamic Bayesian networks[J]. Machine Learning for Cyber Physical Systems, 2020, doi: 10.1007/978-3-662-59084-3-1
29	SERRAS J L , VINGA S , CARVALHO A M . Outlier detection for multivariate time series using dynamic Bayesian networks[J]. Applied Sciences, 2021, 11 (4): 1955.
30	李保雪. 舰艇编队基本作战样式浅析[J]. 中国新通信, 2017, 19 (17): 163- 164.
	LI B X . Analysis of basic combat styles of warship formation[J]. China New Telecommunications, 2017, 19 (17): 163- 164.
31	SONG G B, LIU Z Y, LIU T, et al. Research on the method of warship formation threat assessment based on structural contribution degree[C]//Proc. of the 5th Internationnal Workshop on Advanced Algorithm and Control Engineering, 2021.
32	TIAN L Y , WU J P , WANG Y J . Warships formation identification based on neural network[J]. Journal of Physics: Conference Series, 2020, 1453 (1): 012081.

节点	状态空间
高度	高、中、低
速度	大、中、小
电磁静默\开机	开机、静默
雷达状态	开、关
干扰信号	有、无
发射导弹	是、否
敌我距离	远、中、近
航向	正向(朝向目标)、反向

节点	状态空间
敌我距离	远、中、近
速度	大、中、小
电磁静默/开机	开机、静默
航向	正向(朝向目标)、反向

时间	意图	具体过程
T₁	远征	两个航母群向B岛方向行进
T₂	攻击	干扰机升空释放电磁诱饵, 造成敌群向C岛逼近的假象, 其他战机则准备对B岛进攻
T₃	撤退	我方判明其下一步意图, 并发出警告, 敌方两个航母战斗群从A海域撤退

元意图	高度	速度	电磁信号	雷达状态	航向	敌我距离	干扰信号	是否投弹
起飞	[0.01, 0.3, 0.59]	[0.1, 0.3, 0.6]	[0.1, 0.9]	[0.1, 0.9]	[0.9, 0.1]	[0.01, 0.29, 0.7]	[0.1, 0.9]	[0.05, 0.95]
巡航	[0.7, 0.29, 0.01]	[0.7, 0.29, 0.01]	[0.65, 0.35]	[0.65, 0.35]	[0.75, 0.25]	[0.55, 0.4, 0.05]	[0.65, 0.35]	[0.75, 0.25]
归队	[0.2, 0.45, 0.35]	[0.2, 0.5, 0.3]	[0.9, 0.1]	[0.8, 0.2]	[0.05, 0.95]	[0.15, 0.55, 0.3]	[0.1, 0.9]	[0.1, 0.9]

元意图	速度	电磁信号	距离	朝向
护航	[0.15, 0.75, 0.1]	[0.5, 0.5]	[0.9, 0.09, 0.01]	[0.7, 0.3]
前出	[0.8, 0.19, 0.01]	[0.99, 0.01]	[0.01, 0.19, 0.8]	[0.95, 0.05]
归队	[0.75, 0.15, 0.1]	[0.9, 0.1]	[0.2, 0.4, 0.4]	[0.05, 0.95]

Recognition and prediction of group target intention in multi-domain operations

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算法对比		本文算法		模糊逻辑
算法对比		准确率/%	RMSE	准确率/%	RMSE
识别		87.78	0.231	77.78	0.471
预测	1拍	86.30	0.371	—	—
	4拍	80.37	0.425	—	—
	8拍	72.59	0.505	—	—

算法	推理	预测
本文算法	0.362	0.366
模糊逻辑	0.361	—

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