基于IFA-HFS的雷达波形域LPI性能评估方法

杨诚修; 王谦喆; 彭卫东; 李寰宇; 裴少婷

doi:10.13700/j.bh.1001-5965.2019.0507

基于IFA-HFS的雷达波形域LPI性能评估方法

doi: 10.13700/j.bh.1001-5965.2019.0507

1.
空军工程大学空管领航学院, 西安 710051
2.
空军工程大学航空工程学院, 西安 710038

基金项目:

国家自然科学基金 61773197

详细信息

作者简介:
杨诚修  男, 硕士研究生。主要研究方向:电子综合化工程

王谦喆  男, 硕士, 副教授, 硕士生导师。主要研究方向:电子综合化技术

彭卫东  男, 博士, 教授, 研究生导师。主要研究方向:信号处理

通讯作者:
王谦喆. E-mail:afeu_wqz@163.com

中图分类号: TN955
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出版历程
- 收稿日期: 2019-09-16
- 录用日期: 2019-12-20
- 网络出版日期: 2020-08-20

Radar LPI performance evaluation method for waveform domain based on IFA-HFS

1.
Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China
2.
Aeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China

Funds:

National Natural Science Foundation of China 61773197

More Information

Corresponding author: WANG Qianzhe. E-mail:afeu_wqz@163.com

摘要

摘要:
针对雷达波形域低截获（LPI）性能评估的问题，提出一种应用改进萤火虫算法（IFA）求解指标权重的犹豫模糊集（HFS）评估方法。首先，介绍基于逼近理想解排序（TOPSIS）的犹豫模糊集理论，并从属性和方案2个角度构建指标权重的优化模型；其次，通过引入混沌理论，解决了萤火虫算法容易陷入局部最优的问题，给出用IFA求解指标权重的流程；再次，从雷达发射方角度，提取脉内、脉间5个波形域LPI性能评估指标；最后，得到利用IFA求解指标权重的犹豫模糊集评估方法。选取4种不同类型的雷达进行仿真对比，获得波形域LPI性能排序，验证了方法的快速性和有效性。
- 雷达波形域 /
- 低截获(LPI)性能 /
- 犹豫模糊集(HFS) /
- 指标权重优化 /
- 改进萤火虫算法(IFA)
Abstract:
In order to solve the problem of Low Probability of Interception (LPI) performance evaluation of radar waveform domain, a Hesitant Fuzzy Set (HFS) evaluation method based on Improved Firefly Algorithm (IFA) is proposed to obtain index weight. Firstly, we introduce the HFS theory based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and construct an optimization model of index weights from the perspectives of attribute and scheme. Secondly, we solve the problem that firefly algorithm is easy to fall into local optimum by introducing chaos theory, and give the process to get index weights by using IFA. Then, we extract five LPI performance evaluation indicators between inter-pulse and intra-pulse information from the radar transmitter. Finally, the HFS evaluation method based on IFA is obtained to get optimal index weights. Four different types of radar are selected to simulate and compare the waveform domain LPI performance, and the ranking results are obtained, which verify the rapidity and effectiveness of the proposed algorithm.
- radar waveform domain /
- Low Probability of Interception (LPI) performance /
- Hesitant Fuzzy Set(HFS) /
- index weight optimization /
- Improved Firefly Algorithm (IFA)

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图 1 利用IFA求解指标权重流程图

Figure 1. Flowchart of solving indicator weight by IFA

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图 2 波形域评估指标结构

Figure 2. Structure of waveform domain evaluation indicator

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图 3 LFM信号脉内信息

Figure 3. LFM signal inter-pulse information

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图 4 二相编码信号脉内信息

Figure 4. Two-phase coded signal inter-pulse information

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图 5 IFA优化指标权重的迭代过程

Figure 5. Iterative process of indicator weights optimized by IFA

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表 1 雷达波形域基本参数

Table 1. Basic radar waveform parameters

雷达型号	AN/APQ-7	AN/APG-66	AN/ASG-14	AN/APS-10
名称	机载轰炸瞄准雷达	火力控制雷达	搜索测距雷达	机载搜索雷达
波段	X	X	X	X
体制	脉冲	脉冲多普勒或顺序波瓣转换	脉冲	脉冲
脉冲重复频率/Hz	400, 800, 1 600	900	1 000±25	405, 810
脉冲宽度/μs	0.75, 0.4, 2	0.85	1, 0.5	0.8, 2.2

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表 2 犹豫模糊决策矩阵

Table 2. Hesitant fuzzy decision matrix

雷达型号	C₁	C₂	C₃	C₄	C₅
AN/APQ-7	[0.8, 0.5, 0.4, 0.3, 0.1]	[0.9, 0.4, 0.1]	[0.3, 0.2, 0.1]	[0.6, 0.3]	[0.9, 0.7, 0.6]
AN/APG-66	[0.8, 0.7, 0.6, 0.1]	[0.6, 0.5, 0.4]	[0.8, 0.7, 0.6, 0.5, 0.3]	[0.4, 0.3]	[0.8, 0.7, 0.6, 0.4]
AN/ASG-14	[0.8, 0.7, 0.2, 0.1]	[0.9, 0.7, 0.6, 0.5]	[0.8, 0.7, 0.6, 0.4, 0.1]	[0.9, 0.8, 0.6, 0.5]	[0.5, 0.3]
AN/APS-10	[0.5, 0.4]	[0.8, 0.7, 0.6, 0.4]	[0.8, 0.2, 0.1]	[0.9, 0.8, 0.7, 0.6, 0.4]	[0.5, 0.4, 0.3, 0.1]

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表 3 规范化犹豫模糊决策矩阵

Table 3. Normalized hesitant fuzzy decision matrix

方案	C₁	C₂	C₃	C₄	C₅
A₁	[0.9, 0.7, 0.6, 0.5, 0.2]	[0.9, 0.6, 0.1, 0.1, 0.1]	[0.3, 0.2, 0.1, 0.1, 0.1]	[0.6, 0.3, 0.3, 0.3, 0.3]	[0.9, 0.7, 0.6, 0.6, 0.6]
A₂	[0.9, 0.4, 0.3, 0.2, 0.2]	[0.6, 0.5, 0.4, 0.4, 0.4]	[0.8, 0.7, 0.6, 0.5, 0.3]	[0.4, 0.3, 0.3, 0.3, 0.3]	[0.8, 0.7, 0.6, 0.4, 0.4]
A₃	[0.9, 0.8, 0.3, 0.2, 0.2]	[0.5, 0.4, 0.3, 0.1, 0.1]	[0.8, 0.7, 0.6, 0.4, 0.1]	[0.9, 0.8, 0.6, 0.5, 0.5]	[0.5, 0.3, 0.3, 0.3, 0.3]
A₄	[0.6, 0.5, 0.5, 0.5, 0.5]	[0.6, 0.4, 0.3, 0.2, 0.2]	[0.8, 0.2, 0.1, 0.1, 0.1]	[0.9, 0.8, 0.7, 0.6, 0.4]	[0.5, 0.4, 0.3, 0.1, 0.1]

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表 4 不同模型参数下的最优属性权重

Table 4. Optimal attribute weights under different model parameters

模型中的参数(a, b)	[ω₁, ω₂, ω₃, ω₄, ω₅]
a=1, b=0(M1)	[0.217 3, 0.232 7, 0.183 3, 0.172 8, 0.193 9]
a=0.7, b=0.3(M3)	[0.228 7, 0.211 3, 0.194 2, 0.186 7, 0.179 1]
a=0.5, b=0.5(M3)	[0.201 6, 0.218 4, 0.187 6, 0.196 1, 0.196 3]
a=0.3, b=0.7(M3)	[0.213 9, 0.196 1, 0.203 8, 0.189 5, 0.196 7]
a=0, b=1(M2)	[0.210 3, 0.189 7, 0.204 9, 0.197 4, 0.197 7]

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