基于改进残差神经网络的雷达信号识别方法

doi:10.12305/j.issn.1001-506X.2024.10.13

Abstract

Abstract:

In the case of low signal to noise ratio, radar signal feature extraction is difficult, resulting in low recognition accuracy, a radar signal modulation recognition method based on improved residual neural network is proposed. Firstly, the time-frequency analysis method is used to transform the time-domain signal into a two-dimensional time-frequency image. Then, the image is preprocessed by graying, Gaussian filtering, bilinear interpolation, normalization, etc., as the input of the deep learning model. Finally, an improved residual neural network is built, which uses space and channel reconstruction units to reduce feature redundancy and improve feature extraction accuracy, thereby improving radar signal recognition accuracy under low signal to noise ratio. Simulation results show that when the signal to noise ratio is -8 dB, the overall recognition accuracy of the proposed method for 12 typical radar signals reaches 96.67%, which has good noise robustness and anti-confusion ability.

Key words: emitter signal recognition, time-frequency analysis, deep learning, residual neural network

CLC Number:

TN971

Qianqi NIE, Minghui SHA, Yingshen ZHU. Radar signal recognition method based on improved residual neural network[J]. Systems Engineering and Electronics, 2024, 46(10): 3356-3364.

Figures/Tables 15

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References 30

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信号类型	参数	取值范围
LFM	起始频率/GHz	[4, 5]
NLFM	带宽/MHz	[30, 100]
BPSK	载频/GHz	[4, 5]
BPSK	巴克码长度	{7, 11, 13}
QPSK	载频/GHz	[4, 5]
LFM-BPSK	基准频率/GHz	[4, 5]
LFM-BPSK	子脉冲带宽/MHz	{2, 3, 4, 5, 6}
LFM-FSK	基准频率/MHz	[60, 150]
FSK-PSK	基准频率/MHz	[60, 150]
P1~P4	载频/MHz	[60, 150]
P1~P4	相位控制数	{4, 5, 6, 7, 8}(P2码取偶数)
Costas	基准频率/MHz	[60, 150]
Costas	跳频序列长度	{5, 6, 7, 10}

网络模型	空间复杂度/byte	时间复杂度/s
GoogleNet	6.31M	606.72
VGG19	139.63M	279.44
SENet	11.27M	403.86
ResNeXt50	14.61M	592.11
SCConv-ResNet18	11.03M	330.72

方法	空间复杂度/byte	时间复杂度/s
AlexNet	26.11M	172.70
ResNet	11.17M	364.24
DRN	15.89M	239.89
SCConv-ResNet18	11.03M	330.72

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Radar signal recognition method based on improved residual neural network

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