一种高超声速飞行器攻击意图预测方法

doi:10.19665/j.issn1001-2400.2019.05.016

Abstract

Abstract: Aim

ing at the problem that it is difficult to predict the flight trajectory of hypersonic vehicles with high maneuverability, this paper proposes an attack intention prediction method based on hypersonic target motion characteristics. First, the three motion characteristics of hypersonic vehicles are analyzed: the Markov processes model of the motion state, the yaw angle and the reachable area. In order to predict the attack intention, a dynamic Bayesian network is established to reason out the attack relationship between hypersonic vehicles and attack targets. Finally, the simulation experiment is made. Experimental results show that the dynamic Bayesian networks based on motion characteristics can predict the attack intention and that the prediction method has good real-timeliness and effectiveness.

Key words: hypersonic vehicles, attack intention prediction, Markov processes, Bayesian networks

CLC Number:

TP18

LUO Yi,TAN Xiansi,WANG Hong,QU Zhiguo. Method for predicting the attack intention of hypersonic vehicles[J].Journal of Xidian University, 2019, 46(5): 113-119.

Figures/Tables 8

目标	贝叶斯网络节点	时刻1	时刻2	时刻3
航母	$P (C 1)$	(1,0)	(0,1)	(0,1)
	$P (S 1 \| T 1)$	(34.1%,65.9%)
	$P (A 1 \| T 1)$	(75.4%,24.6%)
海上基地	$P (C 2)$	(1,0)	(0,1)	(0,1)
	$P (S 2 \| T 2)$	(40.7%,59.3%)
	$P (A 2 \| T 2)$	(63.3%,36.7%)
机场	$P (C 3)$	(1,0)	(1,0)	(0,1)
	$P (S 3 \| T 3)$	(76.1%,23.9%)	(54.7%,45.3%)
	$P (A 3 \| T 3)$	(77.2%,22.8%)	(82.8%,17.2%)
指挥中心	$P (C 4)$	(1,0)	(1,0)	(1,0)
	$P (S 4 \| T 4)$	(77.5%,22.5%)	(78.9%,21.1%)	(68.1%,31.9%)
	$P (A 4 \| T 4)$	(65.4%,34.6%)	(84.2%,15.8%)	(91.5%,8.5%)

References 12

[1]	杨鑫, 魏兵, 尹伟科 , 等. 超高速飞行器气动外形对电波传播特性的影响[J]. 西安电子科技大学学报, 2018,45(3):40-44.
	YANG Xin, WEI Bing, YIN Weike , et al. Influence of theAerodynamic Shape of a Hypersonic Vehicle on Characteristics of EM Wave Propagation[J]. Journal of Xidian University, 2018,45(3):40-44.
[2]	KIMMEL R L, ADAMCZAK D W, HARTLEY D , et al. Hypersonic International Flight Research Experimentation-5b Flight Overview[J]. Journal of Spacecraft and Rockets, 2018,55(6):1303-1314.
[3]	杨卫丽, 廖孟豪, 方勇 . 美俄高超声速导弹发展取得突破性进展[J]. 战术导弹技术, 2019(1):15-18.
	YANG Weili, LIAO Menghao, FANG Yong. . The U S. and Russia Have SuccessFully Gained Unprecedented Progress on Hypersonic Missile[J]. Tactical Missile Technology, 2019(1):15-18.
[4]	XU B, SHI Z K, SUN F C , et al. Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle with AOA Constraint and Actuator Faults[J]. IEEE Transactions on Cybernetics, 2019,49(3):1047-1057.
[5]	KEN M A, CATHERINE D, DANIEL W , et al. Inference in the Wild: a Framework for Human Situation Assessment and a Case Study of Air Combat[J]. Cognitive Science, 2018,42(7):2181-2204.
[6]	NOH S, AN K . Decision-making Framework for Automated Driving in Highway Environments[C]// Proceedings of the 2018 IEEE Transactions on Intelligent Transportation Systems. Piscataway: IEEE, 2018: 58-71.
[7]	韩春耀, 熊家军, 张凯 , 等. 高超声速滑翔飞行器轨迹预测分析[J]. 火力与指挥控制, 2019,44(2):80-85.
	HAN Chunyao, XIONG Jiajun, ZHANG Kai , et al. Trajectory Prediction Analysis for Hypersonic Glide Vehicle[J]. Fire Control & Command Control, 2019,44(2):80-85.
[8]	张凯, 熊家军, 李凡 , 等. 基于意图推断的高超声速滑翔目标贝叶斯轨迹预测[J]. 宇航学报, 2018,39(11):78-85.
	ZHANG Kai, XIONG Jiajun, Li Fan , et al. Baysian Trajectory Prediction for a Hypersonic Gliding Reentry Vehicle Based on Intent Inference[J]. Journal of Astronautics, 2018,39(11):78-85.
[9]	梁巨平, 周韬, 周浩 . 再入飞行器平稳滑翔可达区域计算分析[J]. 兵器装备工程学报, 2018,39(5):112-116.
	LIANG Juping, ZHOU Tao, ZHOU Hao . Footprint Generation of Steady Glide Reentry Vehicle[J]. Journal of Ordnance Equipment Engineering, 2018,39(5):112-116.
[10]	毕城, 王玲琳, 刘永信 . 参数自适应动态贝叶斯舰船态势估计算法[J]. 西安电子科技大学学报, 2019,46(2):158-163.
	BI Cheng, WANG Linglin, LIU Yongxin . Algorithm for the Assessment of Ship Situatio Based on the Parameter Adaptive Dynamic Bayesian Network[J]. Journal of Xidian University, 2019,46(2):158-163.
[11]	WU P Y, JULIAN CALEY M, KENDRICK G A , et al. Dynamic Bayesian Network Inferencing for Non-homogeneous Complex Systems[J]. Journal of the Royal Statistical Society: Series C (Applied Statistics), 2017,67(2):417-434.
[12]	KOUROU K, PAPALOUKAS C, FOTIADIS D I , et al. Integration of Pathway Knowledge and Dynamic Bayesian Networks for the Prediction of Oral Cancer Recurrence[J]. IEEE Journal of Biomedical and Health Informatics, 2017,21(2):320-327.

Method for predicting the attack intention of hypersonic vehicles

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 12

Related Articles 4

Metrics

Comments

Recommended 10

目标	时刻1	时刻2	时刻3
航母	(31.6%,68.4%)
海上基地	(35.2%,64.8%)
机场	(73.3%,26.7%)	(51.9%,48.1%)
指挥中心	(62.5%,37.5%)	(76.2%,23.8%)	(66.5%,33.5%)

[1]	LU Wanjie,LAN Chaozhen,LV Liang,SHI Qunshan,XU Qing. Dynamic inference method for the orbit status of space objects [J]. Journal of Xidian University, 2020, 47(3): 72-79.
[2]	LIANG Hongquan;WU Wei. Secure link status routing protocol based on node trustworthiness [J]. Journal of Xidian University, 2016, 43(5): 121-127.
[3]	CHAI Hui-min;WANG Bao-shu. Research on the Bayesian networks model in situation assessment [J]. J4, 2009, 36(3): 491-495.
[4]	WANG Kai-jun;ZHANG Jun-ying;ZHAO Feng;ZHANG Hong-yi. Geometric-pattern dynamic Bayesian networks reasoning gene regulatory networks [J]. J4, 2007, 34(6): 922-925.