智能对抗无人机的干扰组合序列生成算法研究

doi:10.19665/j.issn1001-2400.20230903

Abstract

Abstract:

With the maturity and development of the autonomous navigation flight technology for the unmanned aerial vehicle(UAV),the phenomenon of the unauthorized UAV flying in controlled airspace appears,which brings a great hidden danger to personal safety and causes a certain degree of economic losses.The research of this paper is on improving the effectiveness of adaptive measurement and control and navigation interference in the unknown situation of UAV flight control on the basis of identifying the UAV flight status and real-time evaluation of countermeasure effectiveness,and finally realizing the intelligent countermeasure game between the non-intelligent UAV based on the combination of remote communication interference and navigation and positioning interference.In this paper,a game model of the anti-UAV system(AUS) and UAV confrontation is developed based on the original units of radar detection,GPS navigation positioning,UAV remote communication suppression jamming and GPS navigation suppression and spoofing.The mathematical model is constructed by using deep reinforcement learning and the Markov decision process.Meanwhile,the concept of situation assessment ring for the classification of the UVA flight status is proposed to provide basic information for network sensing jamming effectiveness.The near-end strategy optimization algorithm,maximum entropy optimization algorithm and actor-critic algorithm are respectively used to train the constructed intelligent AUS for many times,and finally the network parameters are generated to generate the intelligent interference combination sequence according to the UAV flight state and countermeasures efficiency.The intelligent interference combination sequences generated by various deep reinforcement learning algorithms in this paper all achieve the initial goal of deceiving UAVs,which verifies the effectiveness of the anti-UAVs system model.The comparison experiment shows that the proposed situation assessment loop is sufficient and effective in the aspect of AUS sensing interference effectiveness.

Key words: deep reinforce learning, UAV, situational awareness, intelligence, confrontation, interference combination

CLC Number:

TN911.7

MA Xiaomeng, GAO Meiguo, YU Mohan, LI Yunjie. Research on the interference combinational sequence generation algorithm for the intelligent countermeasure UAV[J].Journal of Xidian University, 2023, 50(6): 44-61.

Figures/Tables 14

参数	符号	值
雷达有效探测距离/m	d_e	1 000
距离误差均值/m	μ_d(d_ru)	10
距离误差方差/m²	$σ d 2$	1.0
角度误差参数/(°)	a,b	-0.25、0.25
遥控通信压制干扰距离比阈值	$d c t h$	3/2

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反无系统动作	上一态势	当前态势
反无系统动作	上一态势	靠近		返回		悬停		降落		诱骗
	靠近	-1.0	-0.5	X	1.0	X	X	X	X	X	X
		-1.0	-1.0	-0.5	-0.5	2.0	X	X	X	X	X
0100	返回	1.0	1.0	X	1.0	X	X	X	X	X	X
遥控干扰		-1.0	-1.0	-0.5	-0.5	1.0	X	X	X	X	X
0010	悬停	-0.5	-1.0	X	-1.0	X	X	X	X	X	X
遥控+导航		X	-0.5	X	-1.0	2.0	X	3.0	X	X	X
0001	降落	-2.0	-2.0	X	-1.0	X	X	X	X	X	X
导航诱骗		-1.0	-1.0	-1.0	-1.0	1.0	X	-0.5	X	X	4.0
1000	诱骗	-2.0	-2.0	X	-2.0	-2.0	-1.0	X	X	X	X
无干扰		-2.0	X	-2.0	X	-1.0	4.0	X	X	X	4.0

无人机所受干扰类型	无人机飞行策略
无干扰	进攻目标区域
遥控压制或导航压制	依据概率选择进攻或返航
遥控压制+导航压制	悬停后降落
遥控压制+导航欺骗	飞行至欺骗点

Research on the interference combinational sequence generation algorithm for the intelligent countermeasure UAV

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