基于双通道卷积神经网络的雷达信号识别

doi:10.16183/j.cnki.jsjtu.2021.209

摘要/Abstract

摘要：

为解决在低信噪比下特征提取困难、雷达信号识别率低的问题,提出了一种基于Choi-Williams分布(CWD)和多重同步压缩变换(MSST)的双通道卷积神经网络模型.模型通过对雷达信号进行CWD和MSST时频分析,分别获取二维时频图像并进行预处理,然后送入双通道卷积神经网络进行深度特征提取,最后将两路通道获取的特征进行融合,通过卷积神经网络分类器实现对雷达信号的分类识别.仿真结果表明:在信噪比为 -10 dB时,所提模型整体识别准确率能达到96%以上,其在低信噪比下表现优异.

关键词: 低信噪比, Choi-Williams分布, 多重同步压缩变换, 双通道卷积神经网络

Abstract:

In order to solve the problems of difficult feature extraction and low recognition rate of radar signal at low signal-to-noise ratios, a dual channel convolutional neural network model based on Choi-Williams distribution (CWD) and multisynchrosqueezing transform (MSST) is proposed, which obtains two-dimensional time-frequency images by CWD and MSST time-frequency analyses on radar signals. Respectively, the time-frequency images are preprocessed and sequencely fed to a dual channel convolutional neural network for deep feature extraction. Finally, the features acquired by the two channels are fused, and the radar signal is classified and recognized through the convolutional neural network classifier. The simulation results show that when the signal-to-noise ratio is -10 dB, the overall recognition accuracy can reach above 96%, which is excellent at low signal-to-noise ratios.

Key words: low signal-to-noise ratios, Choi-Williams distribution (CWD), multisynchrosqueezing transform (MSST), dual-channel convolutional neural network

中图分类号:

TN971
TN 98

全大英, 陈赟, 唐泽雨, 李世通, 汪晓锋, 金小萍. 基于双通道卷积神经网络的雷达信号识别[J]. 上海交通大学学报, 2022, 56(7): 877-885.

QUAN Daying, CHEN Yun, TANG Zeyu, LI Shitong, WANG Xiaofeng, JIN Xiaoping. Radar Signal Recognition Based on Dual Channel Convolutional Neural Network[J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 877-885.

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雷达信号	仿真参数	取值范围
CW	f_c	U(1/8, 1/4)
LFM, NLFM	f₀	U(1/16, 1/8)
	B	U(1/16, 1/8)
BPSK	f_c	U(1/8, 1/4)
QPSK	f_c	U(1/8, 1/4)
FSK	f_min	U(1/24, 1/20)
LFM/BPSK	f_min	U(1/24, 1/20)
	B	U(1/16, 1/8)
LFM/FSK	f_min	U(1/24, 1/20)
	B	U(1/16, 1/8)
BPSK/FSK	f_min	U(1/24, 1/20)

模块名	网络名	核大小	核个数	步长	激活函数	Dropout
卷积模块1	卷积层	5×5	8	1	ReLU	—
	池化层	2×2	—	2	—	—
	卷积层	4×4	16	1	ReLU	—
	池化层	2×2	—	2	—	0.3
卷积模块2	卷积层	3×3	32	1	ReLU	—
	池化层	2×2	—	2	—	—
	卷积层	1×1	32	1	ReLU	—
	池化层	2×2	—	2	—	0.3