一种参数区间交叉类型的目标识别方法

李双明; 关欣; 赵静; 吴斌

doi:10.13700/j.bh.1001-5965.2019.0442

一种参数区间交叉类型的目标识别方法

doi: 10.13700/j.bh.1001-5965.2019.0442

李双明^{1, 2},
关欣^1, ,,
赵静¹,
吴斌¹

1.
海军航空大学, 烟台 264001
2.
中国人民解放军 92941部队, 葫芦岛 125001

基金项目:

国防科技卓越青年科学基金 2017-JCJQ-ZQ-003

泰山学者工程专项经费 ts201712072

详细信息

作者简介:
李双明  男, 博士研究生。主要研究方向:目标识别技术

关欣  女, 博士, 教授, 博士生导师。主要研究方向:信息融合、电子对抗及智能计算

赵静  女, 博士。主要研究方向:证据理论

吴斌  男, 博士研究生。主要研究方向:无线传感器网络

通讯作者:
关欣, E-mail:gxtongwin@163.com

中图分类号: TN95;C934
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- 被引次数: 0
出版历程
- 收稿日期: 2019-08-16
- 录用日期: 2019-12-15
- 网络出版日期: 2020-07-20

A methodology for target recognition with parameters of interval cross type

LI Shuangming^{1, 2},
GUAN Xin^{1
, ,},
ZHAO Jing¹,
WU Bin¹

1.
Naval Aviation University, Yantai 264001, China
2.
Unit 92941 of PLA, Huludao 125001, China

Funds:

National Defense Science and Technology Excellence Youth Talent Fund 2017-JCJQ-ZQ-003

Taishan Scholar Engineer-ing Special Fund ts201712072

More Information

Corresponding author: GUAN Xin, E-mail:gxtongwin@163.com

摘要

摘要:
针对参数区间为交叉类型的目标识别问题，提出了基于直觉模糊集和云模型的逼近理想点（TOPSIS）识别方法。构建了包含个体类和交叉类的目标数据库模型，根据云模型的多步估计算法，得到未知目标相对已知目标类的确定度，提出了确定度向隶属度和非隶属度的转化方法，基于直觉模糊熵计算特征属性的动态权重，形成了去模糊距离测度的TOPSIS识别方法，应用于辐射源信号识别。仿真结果表明，所提方法对参数区间交叉类型的目标正确识别率较高，具有一定的实际应用价值。
- 区间交叉 /
- 直觉模糊集 /
- 云模型 /
- 动态权重 /
- 逼近理想点(TOPSIS)
Abstract:
Aimed at the problem of target recognition with parameters of interval cross type, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for target recognition based on intuitionistic fuzzy set and cloud model is proposed in this paper. The target database model including individual class and cross class is constructed. According to the multi-step estimation algorithm for cloud model, the certainty degree of an unknown target over a known target class is obtained, and the transformation algorithm from certainty degree to membership and non-membership degree is proposed. The dynamic attribute weight is calculated based on intuitionistic fuzzy entropy. The TOPSIS recognition decision method of defuzzification distance measure is formed. The simulation results indicate that the proposed method has a high accuracy rate for target recognition with parameters of interval cross type and thus has a certain practical application value, when applied to radar emitter recognition.
- interval cross /
- intuitionistic fuzzy set /
- cloud model /
- dynamic weight /
- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

HTML全文

图 1 基于直觉模糊集和云模型的TOPSIS识别方法

Figure 1. TOPSIS recognition method based on intuitionistic fuzzy set and cloud model

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图 2 本文属性权重变化曲线

Figure 2. Attribute weight variation curve in this paper

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图 3 文献[10]R₁类的属性权重变化曲线

Figure 3. Variation curve of attribute weight for R₁ class in Ref.[10]

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图 4 文献[10]R₂类的属性权重变化曲线

Figure 4. Variation curve of attribute weight for R₂ class in Ref.[10]

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图 5 文献[10]R₃类的属性权重变化曲线

Figure 5. Variation curve of attribute weight for R₃ class in Ref.[10]

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图 6 文献[10]R₄类的属性权重变化曲线

Figure 6. Variation curve of attribute weight for R₄ class in Ref.[10]

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图 7 文献[10]R₅类的属性权重变化曲线

Figure 7. Variation curve of attribute weight for R₅ class in Ref.[10]

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表 1 雷达数据库

Table 1. Radar database

序号	个体雷达类	射频频率 RF/MHz	脉冲重复周期 RPI/μs	脉宽 PW/μs
1	R₁	[4 940, 5 160]	[3 680, 3 750]	[0.6, 1.2]
2	R₂	[5 000, 5 220]	[3 630, 3 700]	[0.2, 0.5]
3	R₃	[5 100, 5 420]	[3 580, 3 650]	[0.4, 0.7]
4	R₄	[5 400, 5 520]	[3 730, 3 800]	[0.6, 0.9]
5	R₅	[5 480, 5 620]	[3 490, 3 600]	[1, 1.4]
6	R₁₂	[5 000, 5 160]	[3 680, 3 700]
7	R₁₃	[5 100, 5 160]		[0.6, 0.7]
8	R₁₄		[3 730, 3 750]	[0.6, 0.9]
9	R₁₅			[1, 1.2]
10	R₂₃	[5 100, 5 220]	[3 630, 3 650]	[0.4, 0.5]
11	R₃₄	[5 400, 5 420]		[0.5, 0.7]
12	R₃₅		[3 580, 3 600]
13	R₄₅	[5 480, 5 520]
14	R₁₂₃	[5 100, 5 160]
15	R₁₃₄			[0.6, 0.7]

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表 2 不同云模型的仿真结果

Table 2. Simulation results of different cloud models

模型	本文			文献[10]			文献[9]
模型	参数1	参数2	参数3	参数1	参数2	参数3	参数1	参数2	参数3
正确识别率/%	94.9	93.7	93.1	88	85.2	83.1	84.5	93.5	87.8

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表 3 不同权重计算方法的仿真结果

Table 3. Simulation results of different weight calculation methods

权重计算方法	本文	文献[10]	等权重方法
正确识别率/%	94.2	65.4	93.5

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表 4 仿真环境1的正确识别率

Table 4. Correct recognition rate of simulation environment 1

获取测量值的方式	正确识别率/%
获取测量值的方式	本文方法	文献[9]方法
R₁类区间内随机抽取	93.6	86.4
R₂类区间内随机抽取	89.4	90.3
R₃类区间内随机抽取	94.2	91.5
R₄类区间内随机抽取	94.1	91.8
R₅类区间内随机抽取	99.9	97.9
数据库区间内随机抽取	94.6	92.6

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表 5 仿真环境2的正确识别率

Table 5. Correct recognition rate of simulation environment 2

获取测量值的方式	本文方法正确识别率/%			文献[9]方法正确识别率/%
获取测量值的方式	情况1	情况2	情况3	情况1	情况2	情况3
R₁类区间外随机抽取	82.3	46.6	78	69.3	42.7	44.3
R₂类区间外随机抽取	69.9	54	75	78.8	62.6	55.3
R₃类区间外随机抽取	69	68.7	80.7	73.3	64.9	49.4
R₄类区间外随机抽取	74.3	73	87	63	55.6	50.7
R₅类区间外随机抽取	97.1	98	99.7	90.5	87.3	75.3
数据库区间外随机抽取	78.8	68.5	85.3	75.8	61.5	57

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表 6 方法复杂度分析

Table 6. Analysis of method complexity

复杂度指标	本文	文献[10]	文献[9]
正确识别率/%	92.6	84.5	82.6
总耗时/s	0.482 378	0.244 235	0.438 067

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