不确定场景下基于直觉模糊集和灰色关联度的舰船目标威胁评估方法

doi:10.12382/bgxb.2024.0686

摘要/Abstract

摘要：

为了解决不确定场景下目标威胁评估指标权值难以优化、评估结果抗干扰性差等问题,采用直觉模糊理论、灰色关联度、二次规划求解等方法,提出兼顾主客观评价的海上舰船目标威胁评估方法。围绕舰船编队多维度能力构建两级三层评估指标体系,针对噪声、传感器偏差和认知模糊引起的不确定性,采用直觉模糊集进行评估指标定量化描述和运算操作,定义主客观理想解母序列,分别设计基于客观数据特征和主观专家偏好的灰色关联度;构建综合主客观灰色关联度的属性指标权重优化模型,应用Lagrange方法求解最优权值;考虑外界非预期干扰因素对于评估可信度的影响,采用群广义直觉模糊软集描述专家对干扰程度的判断,以修正评估结果。仿真结果验证了方法的有效性。

关键词: 舰船编队, 威胁评估, 直觉模糊集, 灰色关联度, Lagrange方法, 群广义直觉模糊软集

Abstract:

For the target threat assessment in uncertain scenarios,the weights of target threat assessment indexes are difficult to be optimized,and meanwhile the assessment results are poor in anti-interference performance.To deal with these problems,a target threat assessment approach for maritime ships is proposed by adopting intuitionistic fuzzy theory,grey correlation degree and quadratic programming solving methods,which takes into account both the subjective and objective evaluations.Firstly,a two-level and three-layer assessment index system is constructed according to the multi-dimensional capability of warship formation.The intuitionistic fuzzy set is used to quantitatively describe and deal with the uncertainty assessment indexes caused by noise,sensor bias and cognitive fuzzy.The subjective and objective ideal solution mother sequences are defined,and used for computing the grey correlation degrees based on the characteristics of the objective data and the subjective expert's preference,respectively.Then,an attribute index weight optimization model is constructed,which integrates the subjective and objective grey correlation degrees,and the Lagrange method is adopted to solve the optimal weights.Lastly,the impacts of unexpected external interference factors on the credibility of evaluation are considered,and a group generalized intuitionistic fuzzy soft set is used to describe the expert's judgment on the degree of interference in order to correct the evaluation results.The effectiveness of the proposed approach is verified by the simulated results.

Key words: warship formation, threat assessment, intuitionistic fuzzy set, grey correlation degree, Lagrange method, group generalized intuitionistic fuzzy soft set

中图分类号:

朱奕超, 方峰, 王振亚, 彭冬亮. 不确定场景下基于直觉模糊集和灰色关联度的舰船目标威胁评估方法[J]. 兵工学报, 2025, 46(8): 240686-.

ZHU Yichao, FANG Feng, WANG Zhenya, PENG Dongliang. A Target Threat Assessment Approach Based on Intuitionistic Fuzzy Set and Grey Correlation Degree for Warships in Uncertain Scenarios[J]. Acta Armamentarii, 2025, 46(8): 240686-.

图/表 16

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不确定程度	犹豫度计算参数p
很不确定	0.8
不确定	0.65
一般	0.5
确定	0.3
很确定	0.1

不确定程度	犹豫度计算参数p
很不确定	0.8
不确定	0.65
一般	0.5
确定	0.3
很确定	0.1

编号	C₁₁	C₁₂	C₃₁	C₅₃	C₆₂	相对距离/km
1	航母 (很确定)	完好 (确定)	强 (确定)	较快 (确定)	强 (很确定)	[302,344]
2	航母 (很确定)	完好 (确定)	强 (确定)	快 (确定)	强 (确定)	[311,350]
3	驱逐 (很确定)	完好 (确定)	较强 (很确定)	中等 (一般)	中等 (很确定)	[353,388]
4	驱逐 (确定)	良好 (很确定)	较强 (不确定)	中等 (确定)	较强 (一般)	[345,380]
5	护卫 (一般)	轻伤 (确定)	中等 (确定)	中等 (很确定)	中等 (很确定)	[336,378]
6	护卫 (确定)	轻伤 (很确定)	较强 (不确定)	较慢 (确定)	较弱 (确定)	[290,331]
7	护卫 (不确定)	良好 (确定)	中等 (确定)	较慢 (确定)	较弱 (确定)	[404,442]
8	护卫 (一般)	完好 (一般)	较弱 (一般)	较慢 (很确定)	中等 (很确定)	[410,453]
9	护卫 (确定)	轻伤 (一般)	中等 (确定)	中等 (不确定)	较弱 (一般)	[387,424]

编号	C₁₁	C₁₂	C₃₁	C₅₃	C₆₂	相对距离/km
1	航母 (很确定)	完好 (确定)	强 (确定)	较快 (确定)	强 (很确定)	[302,344]
2	航母 (很确定)	完好 (确定)	强 (确定)	快 (确定)	强 (确定)	[311,350]
3	驱逐 (很确定)	完好 (确定)	较强 (很确定)	中等 (一般)	中等 (很确定)	[353,388]
4	驱逐 (确定)	良好 (很确定)	较强 (不确定)	中等 (确定)	较强 (一般)	[345,380]
5	护卫 (一般)	轻伤 (确定)	中等 (确定)	中等 (很确定)	中等 (很确定)	[336,378]
6	护卫 (确定)	轻伤 (很确定)	较强 (不确定)	较慢 (确定)	较弱 (确定)	[290,331]
7	护卫 (不确定)	良好 (确定)	中等 (确定)	较慢 (确定)	较弱 (确定)	[404,442]
8	护卫 (一般)	完好 (一般)	较弱 (一般)	较慢 (很确定)	中等 (很确定)	[410,453]
9	护卫 (确定)	轻伤 (一般)	中等 (确定)	中等 (不确定)	较弱 (一般)	[387,424]

目标	舰船价值	侦察能力	指控能力	打击能力	电子战能力	拦截能力
T1	<0.900,0.084>	<0.862,0.031>	<0.887,0.053>	<0.848,0.050>	<0.748,0.121>	<0.882,0.046>
T2	<0.900,0.084>	<0.847,0.054>	<0.883,0.064>	<0.824,0.074>	<0.786,0.122>	<0.874,0.058>
T3	<0.772,0.193>	<0.763,0.159>	<0.717,0.227>	<0.749,0.173>	<0.616,0.263>	<0.679,0.236>
T4	<0.700,0.224>	<0.781,0.140>	<0.725,0.117>	<0.766,0.154>	<0.633,0.262>	<0.751,0.140>
T5	<0.666,0.167>	<0.785,0.118>	<0.619,0.240>	<0.771,0.134>	<0.638,0.252>	<0.699,0.195>
T6	<0.500,0.373>	<0.896,0.010>	<0.638,0.080>	<0.877,0.010>	<0.739,0.022>	<0.782,0.047>
T7	<0.560,0.183>	<0.644,0.272>	<0.557,0.328>	<0.629,0.287>	<0.496,0.415>	<0.553,0.346>
T8	<0.666,0.182>	<0.620,0.285>	<0.439,0.332>	<0.605,0.300>	<0.479,0.445>	<0.597,0.341>
T9	<0.560,0.283>	<0.684,0.234>	<0.570,0.312>	<0.669,0.249>	<0.554,0.309>	<0.575,0.277>