基于注意力图剪枝的辐射源信号识别方法

doi:10.12305/j.issn.1001-506X.2025.04.05

Abstract

Abstract:

In order to solve the problems of redundant parameters and large amount of computation in the neural network used for radiation source signal recognition, a method of radiation source signal recognition based on attention map pruning is proposed. The proposed method uses the product of the first-order gradient and feature activation values to measure the effectiveness values of the benchmark network convolution kernels, and removes the convolution kernels with low effectiveness values. In order to avoid the serious degradation of signal recognition performance of subnetworks after pruning, the proposed method introduces attention map knowledge loss in the fine-tuning training stage and constructs a joint loss function to transfer the feature extraction capability of the benchmark network to the subnetwork. The experimental results show that after pruning the benchmark network, the signal recognition accuracy of the subnetwork decreases by only 0.07%, the number of network parameters decreases by 48.7%, and the amount of computation decreases by 80.1%. These outcomes validate that the approach presented in this paper effectively accomplishes network lightweighting while maintaining the accuracy of signal recognition.

Key words: radiation source signal recognition, model pruning, attention map, network complexity

CLC Number:

TN957.51

Huifu WANG, Haibo PAN, Jia LUO, Shifei TAO. Radiation source signal recognition method based on attention map pruning[J]. Systems Engineering and Electronics, 2025, 47(4): 1067-1073.

Figures/Tables 10

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References 29

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信号类型	参数	取值范围
单频	载频f_c	[1/8, 3/8]f_s
线性调频	起始频率f₀	[1/16, 3/16]f_s
偶二次调频	带宽B	[1/16, 3/16]f_s
余弦调制	起始频率f₀	[1/16, 3/16]f_s
余弦调制	带宽B	[1/20, 1/10]f_s
频率编码	基准频率f₀	[1/40, 1/4]f_s
频率编码	跳频序列N_F	{4, 5, 6}
二相编码	载频f_c	[1/16, 3/16]f_s
二相编码	巴克码长度	{5, 7, 11, 13}
线性调频与频率编码混合调制	基准频率f₀	[1/40, 1/4]f_s
线性调频与频率编码混合调制	子码带宽B	[1/40, 1/20]f_s
线性调频与余弦调制混合调制	起始频率f₀	[1/16, 3/16]f_s
线性调频与余弦调制混合调制	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	随机梯度下降
学习率	0.01
权重衰减	0.005
动量	0.9

网络类型	参数内存/KB	FLOPs
基准网络	2 179	263.3
子网络	1 392	52.5

信噪比/dB	基准网络	方法1	方法2	方法3	本文所提方法
-8	79.1	71.0	68.9	73.1	78.7
-5	97.6	93.5	92.1	95.2	97.9
-2	99.8	98.6	98.1	99.1	99.7
1	99.9	98.9	98.5	99.5	99.8
4	99.9	99.2	99.1	99.6	99.9

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Radiation source signal recognition method based on attention map pruning

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