基于残差原型网络的辐射源个体识别

doi:10.12305/j.issn.1001-506X.2023.07.36

Abstract

Abstract:

When deep learning methods are used for specific emitter identification, the existing algorithms are insufficient under the low signal to noise ratio. Meanwhile, they all focus on the inter-class distance but ignore the intra-class compactness. To solve this problem, a residual prototype network is proposed to recognize the differential constellation trace figure of input signals by combining the residual network and prototype learning. In addition, prototype loss is combinedwith the distance-based cross entropy loss to further amplify the inter-class distanceby improving the intra-class compactness. The results show that the proposed algorithm has better recognition performance under the same signal to noise ratio condition through experiments on five ZigBee devices. And the accuracy can reach more than 99% when the signal to noise ratio is higher than 8 dB.

Key words: residual prototype network, prototype learning, specific emitter identification, differential constellation trace figure

CLC Number:

TN975

Chunsheng WANG, Yongmin WANG, Hua XU, Huali ZHU. Specific emitter identification based on residual prototype network[J]. Systems Engineering and Electronics, 2023, 45(7): 2249-2258.

Figures/Tables 12

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网络结构	内部组成	输出
卷积模块1	[3×3, 64]×1	S×S维特征
卷积模块2	$ \left[\begin{array}{l}3 \times 3, 64 \\3 \times 3, 64\end{array}\right] \times 3$	S×S维特征
卷积模块3	$ \left[\begin{array}{l}3 \times 3, 128 \\ 3 \times 3, 128\end{array}\right] \times 4$	$ \frac{S}{2} \times \frac{S}{2}$维特征
卷积模块4	$ \left[\begin{array}{l}3 \times 3, 256 \\ 3 \times 3, 256\end{array}\right] \times 6 $	$ \frac{S}{4} \times \frac{S}{4}$维特征
卷积模块5	$ \left[\begin{array}{l}3 \times 3, 512 \\ 3 \times 3, 512\end{array}\right] \times 3 $	$ \frac{S}{8} \times \frac{S}{8}$维特征
卷积模块6	$ \left[\begin{array}{l}3 \times 3, 512 \\ 3 \times 3, 512\end{array}\right] \times 3 $	$ \frac{S}{16} \times \frac{S}{16}$维特征
池化层	自适应平均池化	1×1维特征
全连接层	(512, 2)	2维特征
损失函数	DCEL+PL	d, η, a_i

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Specific emitter identification based on residual prototype network

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