基于深层残差网络和三元组损失的雷达信号识别方法

doi:10.3969/j.issn.1001-506X.2020.11.12

摘要/Abstract

摘要：

针对分类网络难以有效扩展分类数量的问题,提出了一种基于深层残差网络和三元组损失的雷达信号识别方法。该方法首先将雷达信号作为深层残差网络的输入,通过一维卷积将雷达信号映射到128维欧几里得空间,得到信号的特征向量;然后利用三元组损失函数调整网络参数,使得同类信号之间特征向量的欧式距离减小而不同类别信号之间的距离增大;最后通过基于样本库的识别算法实现对信号的分类识别。实验结果表明,相较于传统的分类网络,该方法在保证识别准确率的同时使得模型能够对分类数量进行有效扩展。

关键词: 雷达信号识别, 深层残差网络, 三元组损失函数, 一维卷积

Abstract:

To solve the problem that the classification network is difficult to effectively expand the number of classifications, a radar signal recognition method based on deep residual network and triplet loss is proposed. This method firstly takes the radar signal as the input of the deep residual network, maps the radar signal to 128-dimensional Euclidean space through one-dimensional convolution, and obtains the signal's eigenvector; then uses the triplet loss function to adjust the network parameters so that the Euclidean distance of feature vectors between homogeneous signals decreases and the distance between different types of signals increases; finally, the classification of the signals is realized through a sample library-based recognition algorithm. Experimental results show that compared with traditional classification networks, this method ensures the accuracy of recognition while enabling the model to effectively expand the number of classifications.

Key words: radar signal recognition, deep residual network, triplet loss function, one-dimensional convolution

中图分类号:

TN974

石礼盟, 杨承志, 吴宏超. 基于深层残差网络和三元组损失的雷达信号识别方法[J]. 系统工程与电子技术, 2020, 42(11): 2506-2512.

Limeng SHI, Chengzhi YANG, Hongchao WU. Radar signal recognition method based on deep residual network and triplet loss[J]. Systems Engineering and Electronics, 2020, 42(11): 2506-2512.

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网络结构	激活函数	输出维度
输入层	无	1 024
归一化层1	无	1 024
一维残差堆栈1	ReLU	32×512
一维残差堆栈2	ReLU	32×256
一维残差堆栈3	ReLU	32×128
一维残差堆栈4	ReLU	32×64
一维残差堆栈5	ReLU	32×32
一维残差堆栈6	ReLU	32×16
平坦层	无	512
全连接层1	ReLU	256
全连接层2	ReLU	128
归一化层2(输出层)	无	128

标签	雷达序号	调制方式	载频/MHz	采样率/MHz	脉宽/μs	编码方式	信噪比/dB	每信噪比下的数量
0	雷达1	CW	300	2 000	0.5	无	-20~10	1 000
1	雷达2	CW	500	2 000	0.3	无	-20~10	1 000
2	雷达3	LFM	400~430	2 000	1.2	无	-20~10	1 000
3	雷达4	LFM	600~650	2 000	0.8	无	-20~10	1 000
4	雷达5	BPSK	300	2 000	0.5	13位巴克码	-20~10	1 000
5	雷达6	BFSK	250、300	2 000	0.7	13位巴克码	-20~10	1 000

参数	数值(方法)
epoch	1 000
batchsize	64
学习率	1e-3
优化器	Adam
距离阈值α	0.2

k值	信噪比/dB
k值	-10	-8	-6	-4	-2
1	0.250	0.436	0.583	0.773	0.892
2	0.249	0.439	0.582	0.778	0.893
3	0.251	0.441	0.588	0.780	0.893
4	0.252	0.446	0.593	0.783	0.894
5	0.254	0.453	0.612	0.784	0.895
＞5	0.254	0.453	0.612	0.784	0.895

标签	雷达序号	调制方式	载频/MHz	采样率/MHz	脉宽/μs	编码方式	信噪比/dB	每信噪比下的数量
6	雷达7	QPSK	400	2 000	0.5	格雷码	-20~10	1 000
7	雷达8	CW	100	2 000	1.0	无	-20~10	1 000
8	雷达9	LFM	300~350	2 000	1.5	无	-20~10	1 000
9	雷达10	BPSK	200	2 000	0.3	13位巴克码	-20~10	1 000
10	雷达11	CW	350	2 000	0.8	无	-20~10	1 000