Radio Frequency Fingerprint Clustering Method in Cross-Receiver Scenarios

LIU Xuewu; LUO Zhenxing; SHANG Junna

doi:10.11959/j.issn.1000-0801.DXKX260093

您当前的位置：

首页 >

文章列表页 >

Radio Frequency Fingerprint Clustering Method in Cross-Receiver Scenarios

更新时间：2026-06-23

- Radio Frequency Fingerprint Clustering Method in Cross-Receiver Scenarios
- Telecommunications Science (2026)
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.电磁空间安全全国重点实验室，浙江嘉兴 314033
- 作者简介：
- 基金信息：
  
  The Second Batch of Graduate Education and Teaching Reform Projects at the Provincial Level in Zhejiang Province during the 14th Five-Year Plan Period(JGCG2024202)
- DOI：10.11959/j.issn.1000-0801.DXKX260093
  CLC： TN92
- Received：06 February 2026，
  
  Revised：2026-04-29，
  
  Accepted：23 June 2026，
- 稿件说明：
移动端阅览
LIU Xuewu, LUO Zhenxing, SHANG Junna. Radio Frequency Fingerprint Clustering Method in Cross-Receiver Scenarios[J/OL]. Telecommunications Science, 2026.
DOI：

LIU Xuewu, LUO Zhenxing, SHANG Junna. Radio Frequency Fingerprint Clustering Method in Cross-Receiver Scenarios[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260093.

摘要

射频指纹的聚类是无线通信设备身份识别的核心技术，但针对跨接收机采集的多域信号联合聚类问题尚未得到有效探索。为解决该问题，提出一种伪标签与域适应网络协同驱动的射频指纹聚类方法。首先，采用简单孪生（simple siamese，Simsiam）网络对源域无标签射频指纹数据进行特征挖掘，利用K-means算法生成伪标签；然后，搭建域适应网络，以带伪标签的源域数据和无标签的目标域数据为输入，使模型学习到域不变特征；最后，采用 K-means 算法对域不变特征进行聚类。以 8 台 USRP 设备为对象，采集跨接收机场景下的射频信号构建实验数据集。结果表明，所提方法在识别准确率、归一化互信息和调整兰德指数三个指标上分别达到了99.72%、0.9918和0.9936，较三种对比方法分别提升47.72%-49.72%、0.33-0.49和0.57-0.64。

Abstract

Radio frequency (RF) fingerprint clustering is a core technology for the identification of wireless communication devices. However

the joint clustering of multi-domain signals collected across different receivers has not been effectively explored. To address this issue

a pseudo-label and domain adaptation network co-driven RF fingerprint clustering method was proposed. First

Simsiam (simple siamese

Simsiam) network was employed to mine features from unlabeled source-domain RF fingerprint data

and the K-means algorithm was used to generate pseudo-labels. Then

a domain adaptation network was constructed

which took the source-domain data with pseudo-labels and the unlabeled target-domain data as input to enable the model to learn domain-invariant features. Finally

the K-means algorithm was applied to cluster the domain-invariant features. An experimental dataset was constructed using RF signals collected from 8 USRP devices in a cross-receiver scenario. The results show that the proposed method achieves 99.72%

0.9918

and 0.9936 in terms of recognition accuracy

normalized mutual information (NMI)

and adjusted Rand index (ARI)

respectively

which are 47.72%–49.72%

0.33–0.49

and 0.57–0.64 higher than those of the three comparison methods.

关键词

Keywords

references

JAGANNATH A , JAGANNATH J , KUMAR P S P V . A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges [J ] . Computer Networks , 2022 , 219 : 109455 .

SOLTANIEH N , NOROUZI Y , YANG Y , et al . A review of radio frequency fingerprinting techniques [J ] . IEEE Journal of Radio Frequency Identification , 2020 , 4 ( 3 ): 222 - 233 .

彭林宁 , 胡爱群 , 朱长明 , 等 . 基于星座轨迹图的射频指纹提取方法 [J ] . 信息安全学报 , 2016 , 1 ( 01 ): 50 - 58 .

PENG L N , HU A Q , ZHU C M , et al . RF Fingerprint Extraction Method Based on Constellation Trajectory Diagrams [J ] . Journal of Information Security , 2016 , 1 ( 01 ): 50 - 58 .

闫文君 , 刘康晟 , 凌青 , 等 . 跨场景辐射源个体识别技术综述 [J ] . 雷达学报 , 2025 , 14 : 1 - 20 .

YAN W J , LIU K S , LIN Q , et al . A Review of Cross-Scenario Individual Radio Emitter Identification Technology [J ] . Journal of Radars , 2025 , 14 : 1 - 20 .

ZHANG W , LIU L T , JIANG Y L , et al . A Specific Emitter Identification Method Based on Dual Neural Networks [C ] // 2024 IEEE 12th Asia-Pacific Conference on Antennas and Propagation (APCAP) . Piscataway : IEEE Press , 2024 : 1 - 2 .

WANG B , GAO N , WANG F . Specific emitter identification based on cnn and transformer [C ] // 2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST) . Piscataway : IEEE Press , 2023 : 571 - 575 .

FAN R , SI C K , HAN Y , et al . Rffsnet-sei: A multidimensional balanced-rffs deep neural network framework for specific emitter identification [J ] . Journal of Systems Engineering and Electronics , 2023 , 35 ( 3 ): 558 - 574 .

YAN G H , HUANG Y F , ZHENG L , et al . Specific Emitter Identification Based on Wavelet Convolution Supervised Contrast Learning [C ] // 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC) . Piscataway : IEEE Press , 2024 : 1002 - 1006 .

WANG J B , DING G R , JIAO Y T , et al . Unsupervised Specific Emitter Identification: A Multi-Scale Feature Adaptive Fusion Contrastive Learning Algorithm [J ] . IEEE Wireless Communications Letters , 2025 , 14 ( 8 ): 2361 - 2365 .

LI X , TANG Y , LI E K , et al . Unsupervised Identification Method of Radio-Frequency Fingerprint Based on Deep Clustering [C ] // 2024 4th International Conference on Intelligent Technology and Embedded Systems (ICITES) . Piscataway : IEEE Press , 2024 : 136 - 141 .

STANKOWICZ J , KUZDEBA S . Unsupervised emitter clustering through deep manifold learning [C ] // 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) . Piscataway : IEEE Press , 2021 : 0732 - 0737 .

ZHOU G X , ZHUANG Y H , DING X H , et al . A Simple Siamese Framework for Vibration Signal Representations [C ] // 2022 IEEE International Conference on Image Processing (ICIP) . Piscataway : IEEE Press , 2022 : 2456 - 2460 .

HAO X Y , FENG Z X , LIU R Y , et al . Contrastive self-supervised clustering for specific emitter identification [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 23 ): 20803 - 20818 .

贾鑫 , 蒋磊 , 郭京京 , 等 . 基于深度聚类的通信辐射源个体识别方法 [J ] . 空军工程大学学报 , 2024 , 25 ( 01 ): 115 - 122 .

JIA X , JIANG L , GUO J J , et al . Specific Emitter Identification Based on Deep Clustering [J ] . Journal of Air Force Engineering University , 2024 , 25 ( 01 ): 115 - 122 .

LIU X W , LUO Z X , SHANG J N . Open-Set Specific Emitter Identification Under Cross-Receiver Conditions [C ] // 2025 17th International Conference on Communication Software and Networks (ICCSN) . Piscataway : IEEE Press , 2025 : 216 - 222 .

ZHANG X L , LI T Y , GONG P , et al . Variable-modulation specific emitter identification with domain adaptation [J ] . IEEE Transactions on Information Forensics and Security , 2022 , 18 : 380 - 395 .

ELMAGHBUB A , HAMDAOUI B . A needle in a haystack: Distinguishable deep neural network features for domain-agnostic device fingerprinting [C ] // 2023 IEEE Conference on Communications and Network Security (CNS) . Piscataway : IEEE Press , 2023 : 1 - 9 .

YANG H F , ZHANG H , WANG H J , et al . A novel approach for unlabeled samples in radiation source identification [J ] . Journal of Systems Engineering and Electronics , 2022 , 33 ( 2 ): 354 - 359 .

CHEN X L , HE K M . Exploring simple siamese representation learning [C ] // Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . Piscataway : IEEE Press , 2021 : 15750 - 15758 .

WANG J , ZHANG B N , ZHANG J , et al . Specific emitter identification based on deep adversarial domain adaptation [C ] // 2021 4th International Conference on Information Communication and Signal Processing (ICICSP) . Piscataway : IEEE Press , 2021 : 104 - 109 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

DA-ResNeXt50 method for radio frequency fingerprint identification based on time-frequency and bispectral feature fusion

Substructure correlation adaptation transfer learning method based on K-means clustering

A big data framework for short-term power load forecasting using heterogenous data

Research on clustering algorithm based 6G typical usage scenarios

Safe hierarchical virtual network mapping method based on clustering

Related Author

ZHANG Jiafei

LEI Fuqiang

SHEN Lei

ZHANG Wei

CHEN Mengdi

Yong ZHANG

Haoshuang LIU

Yingbo CAO

Related Institution

School of Mechanical Engineering, Hangzhou Dianzi University

School of Computer ＆Information Technology, Liaoning Normal University

School of Information Engineering, Huzhou University

Zhejiang Province Key Laboratory of Smart Management ＆Application of Modern Agricultural Resources

Willfar Information Technology Co., Ltd.

⁰