基于二值神经网络的辐射源信号识别方法

doi:10.12305/j.issn.1001-506X.2024.11.33

Abstract

Abstract:

In response to the issues of parameter redundancy and high computational complexity in neural networks used for radiation source signal recognition, a radiation source signal recognition method based on binary neural network is proposed. The method proposes using the utility value of the convolution layer to measure the importance of the neural network's convolution layers. Based on the size of the utility value of the convolution layer, important convolution layers are retained as real values, while the remaining convolution layers are binarized. The experimental results show that when the signal-to-noise ratio is greater than -9 dB, the accuracy of signal recognition of the binary neural network obtained using this method is reduced by 0.5% compared to the real-valued convolutional neural network, while the network parameter memory size is reduced by 83.4%, the network computation is reduced by 83.8%, and the network computing complexity is reduced by 85.8%, and it is easy to deploy on various hardware platforms.

Key words: radiation source signal recognition, binary neural network, convolution layer utility value, network complexity

CLC Number:

TN957.51

Huifu WANG, Mingfei MEI, Liang QI, Heng CHAI, Shifei TAO. Radiation source signal recognition method based on binary neural networks[J]. Systems Engineering and Electronics, 2024, 46(11): 3912-3919.

Figures/Tables 12

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References 31

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信号类型	参数	取值范围
CW	载频f_c	[1/8, 3/8]f_s
LFM	起始频率f₀	[1/16, 3/16]f_s
EQFM	带宽B	[1/16, 3/16]f_s
SFM	起始频率f₀	[1/16, 3/16]f_s
SFM	带宽B	[1/20, 1/10]f_s
COSTAS	基准频率f₀	[1/40, 1/4]f_s
COSTAS	跳频序列N_F	{4, 5, 6}
BPSK	载频f_c	[1/16, 3/16]f_s
BPSK	巴克码长度	{5, 7, 11, 13}
LFM_FSK	基准频率f₀	[1/40, 1/4]f_s
LFM_FSK	子码带宽B	[1/40, 1/20]f_s
LFM_SFM	起始频率f₀	[1/16, 3/16]f_s
LFM_SFM	带宽B	[1/16, 3/16]f_s

超参数	取值
训练轮次	40
批次大小	64
优化器	SGD
学习率	0.01
权重衰减	0.005
动量	0.9

卷积层	效用值
Conv1	0.168 6
Conv2	0.134 9
Conv3	0.150 7
Conv4	0.156 7
Conv5	0.199 6
Conv6	0.189 4

方法	信噪比/dB
方法	-9	-6	-3	0	3
CNN	66.1	93.6	99.7	99.7	99.9
文献[26]	61.1	90.1	98.8	99.6	99.7
本文方法	65.2	93.1	99.1	99.7	99.7

方法	信噪比/dB
方法	-8	-5	-2	1	4
CNN	76.1	96.3	99.7	99.9	99.9
文献[26]	71.6	93.4	99.3	99.7	99.8
本文方法	75.3	96.2	99.3	99.8	99.9

Radiation source signal recognition method based on binary neural networks

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网络	参数内存/KB	运算次数/MACs	运算复杂度/FLOPs
CNN	2 156.2	521.5 M	260.8 M
BNN	357.1	84.04 M	37.1 M

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