基于RS-DBN的电子对抗目标清单生成方法

doi:10.12305/j.issn.1001-506X.2021.09.01

Abstract

Abstract:

Because the types and working methods of electronic countermeasures are diverse and change rapidly, effective information is difficult to obtain completely, and different operational characteristics are presented in different operational stages, so it is difficult to accurately evaluate their ranking by using traditional evaluation methods. Therefore, a target level evaluation method of electronic countermeasures based on random set dynamic Bayesian network is proposed. Firstly, the generation method of electronic countermeasures combat target list is combed, the evaluation index system is determined, and the evaluation system is improved based on the changing characteristics of the combat stage combined with the dynamic Bayesian network. Then, after fully consider the characteristics of incomplete data acquisition in combat, the traditional method of solving node parameters of Bayesian network is extended through the introduction of the random set method, and the more accurate node parameters of dynamic Bayesian network are obtained by using the idea of interval mathematics. Finally, the simulation calculation and result analysis of an example are carried out, and the complexity of the algorithm is discussed by the method of determining the node probability. The results show that the method proposed in this paper is more suitable for military evaluation with incomplete samples, and the evaluation results are basically consistent with actual combat situation. The algorithm used are efficient, applicable and popular.

Key words: electronic warfare, dynamic Bayesian network, random set, interval mathematics, target list

CLC Number:

E837
E917

Luda ZHAO, Bin WANG. Method of electronic countermeasure targets' list generation based on RS-DBN[J]. Systems Engineering and Electronics, 2021, 43(9): 2373-2382.

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节点	状态空间	状态字母代号
目标等级	{一级, 二级, 三级}	{One, Two, Three}
RIS	{高, 中, 低}	{High, Middle, Low}
VAL	{高, 中, 低}	{High, Middle, Low}
DEF	{高, 中, 低}	{High, Middle, Low}
JAM	{高, 中, 低}	{High, Middle, Low}
INF	{高, 中, 低}	{High, Middle, Low}
ENV	{高, 中, 低}	{High, Middle, Low}
UNF	{高, 中, 低}	{High, Middle, Low}
GRA	{一级, 二级, 三级}	{One, Two, Three}
MIN	{高, 中, 低}	{High, Middle, Low}
ADD	{高, 中, 低}	{High, Middle, Low}
THR	{高, 中, 低}	{High, Middle, Low}

X	P(RIS\|X)			P(value\|X)
X	high	middle	low	high	middle	low
One	70	25	5	672	25	78
Two	60	25	15	66	25	9
Three	60	25	15	75	15	10

RIS	P(DEF \| RIS)			P(INF \| RIS)
RIS	high	middle	low	high	middle	low
high	55.323	23.56	21.117	12.46	34.56	52.98
middle	46.456	41.47	12.08	46.65	23.46	29.89
low	53.345	32.67	13.99	24.46	45.57	29.97

value	P(UNF \| value)			P(GRA \| value)			P(MIN \| value)			P(THR \| value)
value	high	middle	low	One	Two	Three	high	middle	low	high	middle	low
high	66.56	24.56	8.88	60	20	20	60	30	10	61.25	22.43	16.32
middle	45.457	45.567	8.976	50	20	30	50	20	30	18.88	29.56	51.56
low	53.45	23.45	23.1	40	22	38	40	20	40	23.67	22.18	54.15

划分区间	[0, 0.1)	[0.1, 0.2)	[0.2, 0.3)	[0.3, 0.4)	[0.4, 0.5)	[0.5, 0.6)	[0.6, 0.7)	[0.7, 0.8)	[0.8, 0.9)	[0.9, 1.0]
ξ_JAM	0.004 18	0.003 26	0.002 29	0.001 31	0.000 30	0.000 71	0.001 71	0.002 68	0.003 63	0.004 54
ξ_ENV	0.134 92	0.116 71	0.100 97	0.087 34	0.075 56	0.065 37	0.056 55	0.048 92	0.042 32	0.036 61

Method of electronic countermeasure targets' list generation based on RS-DBN

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RIS	P(JAM \| RIS)
RIS	high	middle	low
high	12.46	34.56	52.98
middle	46.65	23.46	29.89
low	24.456	45.57	29.974

方法	仿真次数	运算时间	评估误差
上下概率法 (d₁=d₂=10)	119	0.007 4	0.011 9
上下概率法 (d₁=d₂=40)	1 679	0.091 5	0.005 6
蒙特卡罗法	546 962	0.267 8	0.000 1

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