系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (4): 1230-1238.doi: 10.12305/j.issn.1001-506X.2022.04.20
卢盈齐, 范成礼*, 付强, 朱晓雯, 李威
收稿日期:
2020-12-31
出版日期:
2022-04-01
发布日期:
2022-04-01
通讯作者:
范成礼
作者简介:
卢盈齐(1977—), 男, 教授, 博士, 主要研究方向为防空反导作战理论与实践|范成礼(1988—), 女, 副教授, 博士, 主要研究方向为防空反导作战建模与仿真|付强(1988—), 男, 讲师, 博士, 主要研究方向为智能信息处理、信息融合|朱晓雯(1998—), 女, 硕士研究生, 主要研究方向为防空反导作战建模与仿真|李威(1996—), 男, 硕士研究生, 主要研究方向为不确定建模与仿真
基金资助:
Yingqi LU, Chengli FAN*, Qiang FU, Xiaowen ZHU, Wei LI
Received:
2020-12-31
Online:
2022-04-01
Published:
2022-04-01
Contact:
Chengli FAN
摘要:
针对现有反导目标威胁评估方法忽略目标属性犹豫度和权重并造成评估精度不高的问题, 引入直觉模糊粗糙集(intuitionistic fuzzy rough set, IFRS)理论, 提出基于改进IFRS相似度和信息熵的反导作战目标威胁评估方法。首先, 细分IFRS犹豫度, 提出改进的IFRS相似度模型, 并证明其性质。之后, 针对现有的IFRS信息熵存在与直觉事实不相符的问题, 提出基于余弦函数的IFRS信息熵, 确定目标属性权重。在此基础上, 构建并量化反导作战目标威胁评估体系, 并通过比较威胁目标的各个属性值与正、负理想解的相似度, 实现目标威胁排序。仿真实例验证了该方法的可行性和有效性, 可为不确定环境下反导作战威胁评估提供新的参考和尝试。
中图分类号:
卢盈齐, 范成礼, 付强, 朱晓雯, 李威. 基于改进IFRS相似度和信息熵的反导作战目标威胁评估[J]. 系统工程与电子技术, 2022, 44(4): 1230-1238.
Yingqi LU, Chengli FAN, Qiang FU, Xiaowen ZHU, Wei LI. Missile defense target threat assessment based on improved similarity measure and information entropy of IFRS[J]. Systems Engineering and Electronics, 2022, 44(4): 1230-1238.
表4
决策矩阵"
目标 | 目标属性 | ||||
距离r1 | 速度r2 | 辐射温度r3 | RCS r4 | 极化特性r5 | |
x1 | 〈0.20, 0.30, 0.60, 0.10〉 | 〈0.30, 0.70, 0.40, 0.20〉 | 〈0.55, 0.65, 0.40, 0.25〉 | 〈0.25, 0.60, 0.50, 0.35〉 | 〈0.70, 0.80, 0.30, 0.20〉 |
x2 | 〈0.30, 0.75, 0.50, 0.25〉 | 〈0.45, 0.75, 0.30, 0.20〉 | 〈0.70, 0.85, 0.30, 0.10〉 | 〈0.50, 0.85, 0.35, 0.10〉 | 〈0.45, 0.60, 0.55, 0.35〉 |
x3 | 〈0.10, 0.80, 0.65, 0.15〉 | 〈0.30, 0.65, 0.40, 0.10〉 | 〈0.45, 0.60, 0.50, 0.25〉 | 〈0.55, 0.95, 0.25, 0.05〉 | 〈0.35, 0.45, 0.65, 0.55〉 |
x4 | 〈0.25, 0.85, 0.15, 0.10〉 | 〈0.20, 0.70, 0.60, 0.30〉 | 〈0.50, 0.60, 0.45, 0.30〉 | 〈0.60, 0.80, 0.30, 0.15〉 | 〈0.80, 0.90, 0.20, 0.10〉 |
信息熵 | 0.975 6 | 0.992 8 | 0.998 8 | 0.994 3 | 0.999 8 |
属性权重 | 0.203 4 | 0.199 8 | 0.198 7 | 0.199 6 | 0.198 5 |
表5
加权决策矩阵"
目标 | 目标属性 | ||||
距离r1 | 速度r2 | 辐射温度r3 | RCS r4 | 极化特性r5 | |
x1 | 〈0.040 7, 0.061 0, 0.122 0, 0.020 3〉 | 〈0.059 9, 0.139 8, 0.079 9, 0.039 9〉 | 〈0.109 3, 0.129 1, 0.079 5, 0.049 7〉 | 〈0.049 9, 0.119 7, 0.099 8, 0.069 8〉 | 〈0.138 9, 0.158 8, 0.059 5, 0.039 7〉 |
x2 | 〈0.061 0, 0.152 5, 0.101 7, 0.050 8〉 | 〈0.089 9, 0.149 8, 0.059 9, 0.039 9〉 | 〈0.139 1, 0.168 9, 0.16 8 9, 0.019 8〉 | 〈0.099 8, 0.169 6, 0.06 9 8, 0.019 9〉 | 〈0.089 3, 0.119 1, 0.10 9 2, 0.069 5〉 |
x3 | 〈0.020 3, 0.162 7, 0.132 2, 0.030 5〉 | 〈0.059 9, 0.129 9, 0.079 9, 0.199 9〉 | 〈0.089 4, 0.119 2, 0.099 3, 0.049 7〉 | 〈0.109 8, 0.189 6, 0.049 9, 0.099 8〉 | 〈0.069 5, 0.089 3, 0.129 0, 0.109 2〉 |
x4 | 〈0.050 8, 0.172 9, 0.030 5, 0.020 3〉 | 〈0.039 9, 0.139 8, 0.119 9, 0.059 9〉 | 〈0.099 3, 0.119 2, 0.089 4, 0.168 9〉 | 〈0.119 7, 0.159 7, 0.059 9, 0.029 9〉 | 〈0.158 8, 0.178 6, 0.039 7, 0.019 8〉 |
表6
不同方法的目标威胁排序及优越度"
方法 | 目标ζ1 | 目标ζ2 | 目标ζ3 | 目标ζ4 | 排序结果 | SDij/% | ||
文献[ | 0.704 6 | 0.698 3 | 0.691 4 | 0.695 1 | 1>2>4>3 | SD12=0.008 9 | SD24=0.004 6 | SD43=0.005 3 |
文献[ | 0.645 2 | 0.632 6 | 0.623 9 | 0.628 8 | 1>2>4>3 | SD12=0.019 5 | SD24=0.006 0 | SD43=0.007 8 |
文献[ | 0.830 1 | 0.820 9 | 0.811 5 | 0.815 4 | 1>2>4>3 | SD12=0.011 0 | SD24=0.006 7 | SD43=0.004 8 |
文献[ | 0.532 9 | 0.523 6 | 0.519 8 | 0.521 3 | 1>2>4>3 | SD12=0.017 4 | SD24=0.004 4 | SD43=0.002 9 |
文献[ | 0.604 6 | 0.587 4 | 0.583 2 | 0.593 6 | 1>4>2>3 | SD14=0.018 2 | SD42=0.010 4 | SD23=0.007 1 |
本文算法 | 0.513 6 | 0.502 2 | 0.493 2 | 0.498 1 | 1>2>4>3 | SD12=0.022 2 | SD24=0.008 2 | SD43=0.009 8 |
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