Radar Signal Recognition Based on Dual Channel Convolutional Neural Network

doi:10.16183/j.cnki.jsjtu.2021.209

Abstract

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

CLC Number:

TN971
TN 98

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.

Figures/Tables 12

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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