基于1D-2D-GRU-ResNet的辐射源个体识别方法

doi:10.12305/j.issn.1001-506X.2026.02.33

Abstract

Abstract:

In existing radar emitter individual identification algorithms, insufficient feature extraction limits the improvement of classification accuracy. To address the issue, a classification method for specific emitter based on one-dimensional and two-dimensional feature fusion is proposed. This method directly converts one-dimensional sequence into two-dimensional data through Gramian angular field. Then gated recurrent unit （GRU） and improved deep residual networks （ResNet） are used to extract one-dimensional and two-dimensional features respectively. The method leverages the advantages of both raw sequence features and the machine learning capabilities for processing two-dimensional data. The simulation results show that the GRU-ResNet has better feature extraction ability and greatly improves the accuracy of individual emitter recognition. When the number of iterations is 50 times, the recognition accuracy is improved by more than 10 % compared with other networks. The method provides a new idea for specific emitter recognition.

Key words: specific emitter identification, gated recurrent unit（GRU）, deep residual network（ResNet）, feature fusion

CLC Number:

TN 911.7

Hengyan LIU, Jun FANG, Qing LING, Wenjun YAN, Keyuan YU, Limin ZHANG. Emitter individual identification method based on 1D-2D-GRU-ResNet[J]. Systems Engineering and Electronics, 2026, 48(2): 727-735.

Figures/Tables 10

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层（阶段）	网络结构
阶段0	Cov2d（1, 7, 64）, BN, ReLU, MaxPool2d
阶段1	1个卷积模块，2个识别模块
阶段2	1个卷积模块，3个识别模块
阶段3	1个卷积模块，5个识别模块
阶段4	1个卷积模块，2个识别模块
池化层	AdaptiveAvgPool2d（1, 1）
降维层	squeeze（）
Pro1	Linear（2048, 128）, BN, ReLU, （Linear Block） Linear （128, 128） , BN, ReLU，（Linear Block） Linear （128, 64） , BN
Pro2	Linear （64, 64） , BN, ReLU Linear （64, 32） , BN

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Emitter individual identification method based on 1D-2D-GRU-ResNet

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模型	时间复杂度	Num
GRU	$ O(N \cdot ({n_{{\mathrm{input}}}} \cdot {n_{{\mathrm{hidden}}}} + n_{{\mathrm{hidden}}}^2)) $	−
改进ResNet	$ O\left( \displaystyle\sum\nolimits_{l = 1}^{55} {{F_l} \cdot {C_l} \cdot {K_l} \cdot } {H_l} \cdot {W_l}\right) $	96
GRU-ResNet	$ O\left(\displaystyle \sum\nolimits_{l = 1}^{55} {{F_l} \cdot {C_l} \cdot {K_l} \cdot } {H_l} \cdot {W_l} \right) $	18
Linformer-ResNet	$ O\left( \displaystyle \sum\nolimits_{l = 1}^{55} {{F_l} \cdot {C_l} \cdot {K_l} \cdot } {H_l} \cdot {W_l}\right) $	50