一种结合多尺度小波与超球面表示的射频指纹开集识别方法

田欣玉; 李子睿; 郑庆河; 周福辉; 余礼苏; 黄崇文; 姜蔚蔚; 束锋; 赵毅哲

doi:10.11999/JEIT260214

一种结合多尺度小波与超球面表示的射频指纹开集识别方法

doi: 10.11999/JEIT260214 cstr: 32379.14.JEIT260214

1.
山东管理学院智能工程学院济南 250357
2.
南京航空航天大学人工智能学院南京 211106
3.
南昌大学信息工程学院南昌 330031
4.
浙江大学信息与电子工程学院杭州 310058
5.
北京邮电大学信息与通信工程学院北京 100876
6.
海南大学信息与通信工程学院海口 570228
7.
电子科技大学信息与通信工程学院成都 611731

基金项目: 国家自然科学基金(62401070)，山东省自然科学基金(ZR2019ZD01, ZR2023QF125)，山东省高等学校青年创新团队计划(2024KJH005)，山东省科技型中小企业创新能力提升工程(2024TSGC0055)

详细信息

作者简介:
田欣玉：女，讲师，研究方向为人工智能、模式识别、信号处理、物联网

李子睿：男，本科生，研究方向为无线通信、射频指纹识别、物联网、人工智能

郑庆河：男，教授，研究方向为无线通信、认知无线电、机器学习、调制识别

周福辉：男，教授，研究方向为电磁空间机器学习基础理论、认知智能与知识图谱、频谱智能共享和动态接入

余礼苏：男，副教授，研究方向为射频光载无线通信、非正交多址接入、无人机通信、人工智能

黄崇文：男，教授，研究方向为6G无线通信、智能协同感知、智能天线

姜蔚蔚：男，副教授，研究方向为卫星通信、无线通信、物联网、人工智能

束锋：男，教授，研究方向为智能无线通信、信息安全、大规模MIMO测向与定位

赵毅哲：男，副教授，研究方向为无线通信、通信控制一体化、流体天线

通讯作者:
郑庆河　zqh@sdmu.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2026-02-28
- 修回日期: 2026-03-27
- 录用日期: 2026-03-27
- 网络出版日期: 2026-04-22

A Radio Frequency Fingerprint Open-set Identification MethodCombining Multi-scale Wavelet Front-end and Hyperspherical Metric Learning

1.
School of Intelligent Engineering, Shandong Management University, Jinan 250357, China
2.
School of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3.
School of Information Engineering, Nanchang University, Nanchang 330031, China
4.
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
5.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
6.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
7.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (62401070), Shandong Provincial Natural Science Foundation (ZR2019ZD01, ZR2023QF125), Shandong Provincial Youth Innovation Team Plan of Higher Education Institutions (2024KJH005), Shandong Provincial Science and Technology Based Small and Medium sized Enterprises Innovation Capability Enhancement Project (2024TSGC0055)

摘要

摘要: 针对低信噪比环境下射频指纹特征易被噪声掩盖、多径效应引发非线性失真，以及现有方法在特征提取与未知设备检测能力上的不足，该文提出一种结合多尺度小波与超球面表示的射频指纹开集识别方法。首先，设计基于一维平稳小波变换的多尺度特征提取前端，实现对I/Q信号的全分辨率、多尺度分解，为后续网络提供高判别性输入。其次，构建多尺度残差注意力网络，融合深度残差学习、全局自注意力机制与双向长短时记忆网络，增强模型对微弱指纹特征的感知能力与长程时序依赖建模能力。最后，引入超球面度量学习，将特征空间约束至单位超球面，通过优化角度间隔构建类内紧凑、类间可分离的特征分布，并基于余弦相似度实现未知设备的有效检测。在基于IEEE 802.11协议的高保真仿真数据集上的实验结果表明，所提方法在–5～20 dB信噪比范围内均能保持较高的开集识别准确率，平均分类准确率达65.34%，在–5 dB低信噪比下曲线下面积(AUC)达0.81，显著优于现有对比方法，验证了其在极端恶劣信道环境下的鲁棒性与有效性。
- 射频指纹识别 /
- 多尺度小波 /
- 超球面表示 /
- 多尺度残差注意力
Abstract: Objective Open-set Radio Frequency Fingerprint (RFF) identification under low Signal-to-Noise Ratio (SNR) conditions is challenging because fingerprint features are easily masked by noise, multipath effects induce nonlinear distortions, and existing methods struggle with feature extraction and unknown device detection. This study proposes a deep learning framework that integrates a multi-scale wavelet front-end with hyperspherical metric learning to achieve robust open-set RFF identification. Methods The proposed method, MS-RANet, comprises three key components. First, a multi-scale wavelet front-end based on one-dimensional stationary wavelet transform performs full-resolution, multi-scale decomposition of I/Q signals, preserving discriminative fingerprint information while suppressing noise. Second, a multi-scale residual attention network incorporates deep residual learning, global self-attention, and Bidirectional LSTM (BiLSTM) to enhance sensitivity to subtle fingerprint features and capture long-range temporal dependencies. Third, hyperspherical metric learning constrains the feature space onto a unit hypersphere, optimizing angular margins to produce compact intra-class and separable inter-class feature distributions. Unknown devices are subsequently detected using cosine similarity. Results and Discussions Experiments on a high-fidelity IEEE 802.11 simulation dataset demonstrate the effectiveness of MS-RANet. The method achieves an average classification accuracy of 65.34% across SNR levels from –5 dB to 20 dB, and an Area Under the Curve (AUC) of 0.81 at –5 dB SNR, outperforming DNN, GRU, CNN-LSTM, ResNet50, and DRSN-CA. Confusion matrices and Receiver Operating Characteristic (ROC) curves confirm robustness under extreme channel conditions. t-SNE visualization shows well-separated, compact clusters for known devices, while unknown samples are effectively isolated from known class regions. Ablation studies verify the contributions of the multi-scale wavelet front-end, global attention, BiLSTM, and hyperspherical metric learning modules. Conclusions This study presents a robust open-set RFF identification method combining a multi-scale wavelet front-end with hyperspherical metric learning. The framework exhibits strong noise resilience, enhanced feature discrimination, and reliable detection of unknown devices under low-SNR and multipath fading conditions. Future work will focus on reducing computational complexity, improving inference speed, evaluating generalization across diverse scenarios and protocols, and integrating the method with complementary physical-layer security mechanisms for collaborative authentication.
- Radio frequency fingerprint identification /
- Multi-scale wavelet /
- Hyperspherical metric learning /
- Multi scale residual attention

HTML全文

图 1 通信系统信号模型

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图 2 结合多尺度小波与超球面表示的RFF开集识别架构

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图 3 多尺度特征提取前端结构

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图 4 多尺度残差注意力网络结构

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图 5 MS-RANet模型训练过程的收敛曲线

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图 6 典型信噪比下所提方法MS-RANet的混淆矩阵

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图 7 典型信噪比下各个方法的ROC曲线

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图 8 基于t-SNE的特征可视化对比(SNR = 20 dB)

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表 1 信号模型参数

参数类型	参数设置	参数类型	参数设置
通信协议	IEEE 802.11	帧类型	L-LTF
载波频率(GHz)	5	信道带宽(MHz)	20
采样长度(Points)	160	信噪比(dB)	–5:5:20
指纹参数$ \alpha $	[0.7, 1.3]	指纹参数$ \beta $	$ \approx \alpha $–1
信道类型	多径瑞利衰落	直流偏移(dB)	[–50, –32]
设备数(已知/未知)	67/20	频率偏移(ppm)	[–4, 4]
样本划分(Train: Val: Test)	8:1:1	相位噪声(rad)	[0.01, 0.3]
样本总量(Frames)	87000	噪声类型	加性高斯白噪声

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表 2 训练过程超参数

参数名称	数值	参数名称	数值
初始学习率	0.0008	最大迭代轮数	30
学习率衰减因子	0.6	丢弃率$ \rho $	0.3
学习率衰减周期	8	超球面半径$ s $	16
惩罚因子$ \lambda $	0.0001	特征空间维度$ d $	256
批量大小$ \varOmega $	96	梯度裁剪阈值	1

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表 3 RFF识别性能对比

对比模型	平均准确率(%)	AUC(SNR = –5 dB)	参数量(M)	计算量(GFLOPs)	推理时间(ms)
DNN^[20]	35.56	0.51	4.85	0.07	1.3
GRU^[21]	54.27	0.58	0.86	6.54	7.8
CNN-LSTM^[22]	54.35	0.67	1.15	8.12	6.4
ResNet50^[23]	56.44	0.62	23.5	4.13	9.6
DRSN-CA^[24]	58.92	0.73	1.65	2.45	7.2
本文MS-RANet	65.34	0.81	0.34	2.58	10.5

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表 4 MS-RANet模块消融实验结果

序号	MS-FE 模块	BiLSTM 模块	全局注意力模块	HML 模块	平均准确率(%)	AUC (SNR=–5 dB)
1	×	×	×	×	35.61	0.53
2	√	×	×	×	57.85	0.68
3	√	√	×	×	59.23	0.70
4	√	√	√	×	60.15	0.72
5	√	√	√	√	65.64	0.79

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