Radar Signal Modulation Type Recognition Based on Attention Mechanism Enhanced Residual Networks

doi:10.12382/bgxb.2022.0302

Abstract

Abstract:

To solve the problem of low recognition rate of radar signal modulation type under the condition of low SNR in intelligence analysis, an radar signal modulation type recognition method based on attention mechanism enhanced residual networks is proposed. Firstly, drawing on the strong energy aggregation characteristics of the smooth pseudo Wigner-Ville distribution time-frequency transform, the signal modulation type is transformed into a two-dimensional time-frequency image. Then, residual networks composed of a two-layer convolution network and a six-layer residual block are built, and the convolutional block attention module is inserted between the networks to enhance the attention to features and improve the effectiveness of feature extraction. Finally, two-dimensional time-frequency images are input into the network model to realize modulation type recognition. The simulation results show that this method can effectively extract the feature of time-frequency images for six typical radar signal modulation types, and can achieve 100% accuracy above 0dB Signal-Noise Ratio, and still maintain 94.2% accuracy under -10dB Signal-Noise Ratio.

Key words: signal modulation type recognition, smooth pseudo Wigner-Ville distribution, convolutional neural network, residual network, attention mechanism

CLC Number:

TN97.1

WU Liyang, GUO Pengcheng, LIU Chao, LI Wenqiang. Radar Signal Modulation Type Recognition Based on Attention Mechanism Enhanced Residual Networks[J]. Acta Armamentarii, 2023, 44(8): 2310-2318.

Figures/Tables 16

References 20

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基础层	分支层	输入尺寸× 通道数	输出尺寸× 通道数
	Conv2d_1卷积层	56×56×1	56×56×32
基础网络块1	Conv2d_2卷积层	56×56×32	56×56×64
	CBAM模块	56×56×64	56×56×64
	Residual_1残差块	56×56×64	56×56×64
基础网络块2	Residual_2残差块	56×56×64	28×28×128
	CBAM模块	28×28×128	28×28×128
	Residual_1残差块	28×28×128	28×28×128
基础网络块3	Residual_2残差块	28×28×128	14×14×256
	CBAM模块	14×14×256	14×14×256
	Residual_1残差块	14×14×256	14×14×256
基础网络块4	Residual_2残差块	14×14×256	7×7×512
	CBAM模块	7×7×512	7×7×512
全局平均池化层		7×7×512	1×1×512
线性层		1×1×512	1×1×6

基础层	分支层	输入尺寸× 通道数	输出尺寸× 通道数
	Conv2d_1卷积层	56×56×1	56×56×32
基础网络块1	Conv2d_2卷积层	56×56×32	56×56×64
	CBAM模块	56×56×64	56×56×64
	Residual_1残差块	56×56×64	56×56×64
基础网络块2	Residual_2残差块	56×56×64	28×28×128
	CBAM模块	28×28×128	28×28×128
	Residual_1残差块	28×28×128	28×28×128
基础网络块3	Residual_2残差块	28×28×128	14×14×256
	CBAM模块	14×14×256	14×14×256
	Residual_1残差块	14×14×256	14×14×256
基础网络块4	Residual_2残差块	14×14×256	7×7×512
	CBAM模块	7×7×512	7×7×512
全局平均池化层		7×7×512	1×1×512
线性层		1×1×512	1×1×6

类型	载频/ MHz	采样频率/ MHz	(子)脉宽/ ms	巴克码
CW	7~12	60	1~5
BPSK	7~12	60		7,11,13
LFM	10	60	8~16
FSK	9~12	60	1
NLFM	7~12	60	8~16
QPSK	7~12	60	1

类型	载频/ MHz	采样频率/ MHz	(子)脉宽/ ms	巴克码
CW	7~12	60	1~5
BPSK	7~12	60		7,11,13
LFM	10	60	8~16
FSK	9~12	60	1
NLFM	7~12	60	8~16
QPSK	7~12	60	1

识别算法	信噪比/dB
识别算法	-20	-10	0
LeNet-FisherDDL^[11]	45.6	67.7	98.0
MRSAF-DBN^[19]	51.4	73.5	98.4
Chrip-Zernike-ResNet^[20]	58.6	70.1	99.1
本文算法	63.1	94.2	100