基于迁移学习的小样本OFDM目标增强识别方法

doi:10.16183/j.cnki.jsjtu.2022.041

摘要/Abstract

摘要：

在非合作场景所导致的小样本条件下,稳健提取通信辐射源目标特征并准确识别目标是当前研究的难点和热点.针对正交频分复用通信辐射源的小样本个体识别问题,文章在相位域、时域翻转的数据增强和源领域实例迁移的基础上,提出一种非合作通信辐射源个体识别方法.采用不同域翻转的数据增强方法扩充数据集,结合改进的残差网络,达到提高正交频分复用通信辐射源个体识别准确率的目的,并引入迁移学习以增强识别模型的泛化能力.实验结果表明:数据增强策略提升了小样本下的正交频分复用通信辐射源个体识别准确率,迁移学习方法的引入加快了模型的收敛速度,小幅度提高模型识别准确率并提升了鲁棒性.

关键词: 正交频分复用, 小样本识别, 数据增强, 迁移学习, 深度学习, 目标识别

Abstract:

Under the few-shots condition caused by non-cooperative scenes, robust extraction of communication emitter features and accurate identification of targets are the difficulties and hotspots of current research. Aimed at the problem of emitter identification under the few-shots condition of orthogonal frequency division multiplexing (OFDM) signals, this paper proposes a non-cooperative target identification method based on phase/time domain flipping data augmentation and source domain instance-based transfer learning. The data set is expanded by different domain flipping data augmentation methods, and the improved residual network is applied to achieve the purpose of promoting the identification rate of the OFDM emitter. Then, transfer learning is introduced to strengthen the generalization ability of the identification model. The experimental results show that the data augmentation method can significantly improve the OFDM emitter identification rate under the few-shots condition. Furthermore, the transfer learning method accelerates the convergence speed, slightly increases the recognition rate, and improves robustness of the model.

Key words: orthogonal frequency division multiplexing (OFDM), few-shots identification, data augmentation, transfer learning, deep learning, target identification

中图分类号:

TN929.5
TP18

唐泽宇, 邹小虎, 李鹏飞, 张伟, 余佳奇, 赵耀东. 基于迁移学习的小样本OFDM目标增强识别方法[J]. 上海交通大学学报, 2022, 56(12): 1666-1674.

TANG Zeyu, ZOU Xiaohu, LI Pengfei, ZHANG Wei, YU Jiaqi, ZHAO Yaodong. A Few-Shots OFDM Target Augmented Identification Method Based on Transfer Learning[J]. Journal of Shanghai Jiao Tong University, 2022, 56(12): 1666-1674.

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数据输入方式	平均识别准确率 ± 标准差
图像	32.23±2.78
实部	51.39±3.25
实部和虚部组合	51.57±3.34
幅值	66.16±3.45
幅值和相位组合	67.28±2.80

卷积核结构	平均识别准确率±标准差
7×7原卷积核	67.28±2.90
1×7调整后卷积核	69.83±1.48

数据增强方法	平均识别准确率±标准差
无数据增强	69.83±1.48
子载波调序	77.30±2.24
添加高斯噪声	77.69±2.38
相位域翻转	78.48±2.14
时域翻转	79.35±1.26

迁移学习作用对比	平均识别准确率±标准差
无迁移学习	79.35±1.26
有迁移学习(源域为5部小米手机)	77.76±2.06
有迁移学习(源域为5部vivo手机)	76.60±1.94