基于高斯平滑与模糊函数等高线的雷达辐射源信号分选

侯文太; 普运伟; 郭媛蒲; 马蓝宇

doi:10.16383/j.aas.c180739

基于高斯平滑与模糊函数等高线的雷达辐射源信号分选

doi: 10.16383/j.aas.c180739

侯文太^1,,
普运伟^{1, 2,},
郭媛蒲^1,,
马蓝宇^1,

1.
昆明理工大学信息工程与自动化学院昆明 650500
2.
昆明理工大学计算中心昆明 650500

基金项目:

国家自然科学基金 61561028

详细信息

作者简介:
侯文太  昆明理工大学硕士研究生.2016年获得南京航空航天大学学士学位.主要研究方向为智能信号处理, 模式识别. E-mail: vintage_hou@foxmail.com

普运伟  昆明理工大学教授.2007年获得西南交通大学博士学位.主要研究方向为智能信号处理, 模式识别. 本文通信作者. E-mail: puyunwei@126.com

郭媛蒲  昆明理工大学硕士研究生.2016年获得南京工程学院学士学位.主要研究方向为智能信号处理, 模式识别. E-mail: guoyuanpu@foxmail.com

马蓝宇  昆明理工大学硕士研究生. 2016年获得湖北工程学院学士学位.主要研究方向为智能信号处理与模式识别. E-mail: Raveler@foxmail.com

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出版历程
- 收稿日期: 2018-11-06
- 录用日期: 2019-01-18
- 刊出日期: 2021-10-26

A Sorting Method for Radar Emitter Signals Based on the Gaussian Smoothing and Contour Lines of Ambiguity Function

HOU Wen-Tai^1
,,
PU Yun-Wei^{1, 2
,},
GUO Yuan-Pu^1
,,
MA Lan-Yu^1
,

1.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500
2.
Computer Center, Kunming University of Science and Technology, Kunming 650500

Funds:

National Science Foundation of China 61561028

More Information

Author Bio:
HOU Wen-Tai  Master student at Kunming University of Science and Technology. He received his bachelor from Nanjing University of Aeronautics and Astronautics in 2016. His research interest covers intelligent signal processing and pattern recognition

PU Yun-Wei  Professor at Kunming University of Science and Technology. He received his Ph.D. degree from Southwestern Jiaotong University in 2007. His research interest covers intelligent signal processing and pattern recognition

GUO Yuan-Pu  Master student at Kunming University of Science and Technology. She received her bachelor degree from Nanjing institute of Technology University in 2016. Her research interest covers intelligent signal processing and pattern recognition

MA Lan-Yu  Master student at Kunming University of Science and Technology. He received his bachelor degree from Hubei Engineering University in 2012. His research interest covers intelligent signal processing and pattern recognition

摘要

摘要: 雷达辐射源信号分选是电子侦察系统、威胁告警系统的关键步骤.针对现有基于模糊函数的复杂体制雷达辐射源信号分选方法信息利用率低、易受噪声影响等问题, 提出一种基于模糊函数等高线的分选新方法; 首先, 对信号的模糊函数进行高斯平滑处理并绘制其等高线作为进一步的特征提取对象; 其次, 从图像处理的角度提取正外接矩和方向角作为雷达信号分选的特征向量; 最后, 用核模糊C均值聚类算法对特征向量进行分选.仿真实验表明, 所提方法在8 dB以上的固定信噪比环境下分选6类典型信号的成功率均为100 %, 即使在0 dB环境下, 分选成功率也保持在89.04 %以上; 在0 ~ 20 dB动态信噪比环境下分选成功率达到96.36 %.实测数据验证, 所提特征提高了5种外场辐射源信号的分选效果, 可作为经典5参数的有效补充. 此外, 所提特征还具备较低的计算量, 提取单个信号特征的耗时仅为0.24 s, 具有一定的工程价值.
- 雷达辐射源 /
- 信号分选 /
- 模糊函数 /
- 高斯平滑 /
- 图像特征提取 /
- 核聚类
Abstract: Sorting radar emitter signals is the key step of electronic reconnaissance system and threat warning system. Existing radar emitter signal sorting methods based on ambiguity function have many problems, such as low information utilization and susceptibility to noise. In this paper, a novel sorting method based on contour lines of ambiguity function is proposed. First, an ambiguity function is processed by using a Gaussian operator and contour lines are created as object of feature extraction. Then, a positive bounding rectangle and a direction angle are extracted as the feature vector from the perspective of image processing. Finally, the kernel fuzzy C-means clustering algorithm is used to sort the feature vector. The simulated experiments show that the success rate of sorting six kinds of typical signals by proposed method not only achieves 100 % in fixed SNR environment above 8 dB, but also keeps above 89.04 % in 0 dB. In addition, the measured data show that the proposed features can improve the sorting effect of five kinds of outfield emitter signals, which can be used as an effective complement to the classical five parameters. Besides, the proposed feature also has lower computational complexity, the feature extraction time of one signal is only 0.24 s, which indicated the engineering value of the proposed method.
- Radar emitter /
- signal sorting /
- ambiguity function /
- Gaussian smoothing /
- image feature extraction /
- kernelized clustering
Recommended by Associate Editor PAN Quan
注释:

1) 本文责任编委潘泉

HTML全文

图 1 CON信号的AF在0 dB和20 dB下的平滑效果(σ = 1;M = 5)

Fig. 1 Smoothing efiect of AF of CON in 20 dB and 0 dB (σ = 1; M = 5)

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图 2 6类典型信号的AF等高线

Fig. 2 AF contour lines of six typical signals

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图 3 LFM信号的正外接矩

Fig. 3 Positive bounding rectangle of LFM

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图 4 LFM信号的方向角

Fig. 4 Direction angle of LFM

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图 5 分选成功率对比

Fig. 5 Comparison of successful rate

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图 6 信号集2的分选效果图

Fig. 6 Sorting efiect diagram of signal set2

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表 1 信号集1的平均分选成功率(%)

Table 1 Average correct rate of signal set1 (%)

信号类型	不同SNR下分选成功率(dB)
信号类型	0	2	4	6	8~20
CON	100	100	100	100	100
LFM	100	100	100	100	100
BPSK	82.54	96.69	98.03	99.1	100
QPSK	83.32	97.06	98.22	98.86	100
MSEQ	87.77	97.42	99.01	99.54	100
BFSK	80.58	100	100	100	100
平均	89.04	98.53	99.21	99.58	100

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表 2 信号集2的分选结果

Table 2 Sorting results of signal set2

信号类型	分类结果
信号类型	CON	LFM	BPSK	QPSK	MSEQ	BFSK
hline CON	110	0	0	0	0	0
LFM	0	110	0	0	0	0
BPSK	0	0	108	3	4	2
QPSK	0	0	2	107	0	13
MSEQ	0	0	0	0	106	0
BFSK	0	0	0	0	0	95
成功率%	100 %	100 %	98.18 %	97.27 %	96.36 %	86.36 %

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表 3 实测雷达数据参数分布

Table 3 Distribution of measured radar parameters

辐射源	辐射源参数
辐射源	调制类型	RF(MHz)	PW(μs)
1	线性调频	9 810、9 682、9 645、9 750、9 662五个频点波位组变, 频率分集	20
2	非线性调制	9 850固定	16
3	非线性调制	9 807、9 833、9 792、9 822、9 762、9 773六个频点波位组变	3 ~ 5个脉冲一组, 每组PW在7、13任意
4	常规脉冲	9 500 ~ 9 700单脉冲捷变	3 ~ 5个脉冲一组, 每组PW在0.9、1.0、1.1、1.2任意
5	线性调频	9 513、9 518、9 523、9 548、9 553、9 563六个频点波位组变	3 ~ 5个脉冲一组, 每组PW在6、12、18任意

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表 4 实测雷达数据分选结果(%)

Table 4 Sorting results of measured radar data (%)

特征	不同辐射源的分选成功率
特征	1	2	3	4	5
RF、PW	100	100	0	57	40
S, AR, α, RF, PW	100	100	56	63	92

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表 5 特征提取耗时对比(s)

Table 5 Timing comparison of the feature extration (s)

分选特征	不同信号特征的提取耗时						平均
分选特征	CON	LFM	BPSK	QPSK	MSEQ	BFSK	平均
文献[6]	0.15	0.12	0.18	0.18	0.15	0.15	0.16
文献[7]	3.69	3.58	3.78	3.52	3.94	3.50	3.67
文献[9]	9.58	9.59	9.59	9.55	9.55	9.53	9.56
文献[12]	0.18	0.19	0.20	0.20	0.19	0.21	0.19
本文特征	0.23	0.23	0.23	0.28	0.23	0.25	0.24

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