跨接收机场景下的射频指纹聚类方法

刘雪武; 骆振兴; 尚俊娜

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

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跨接收机场景下的射频指纹聚类方法

更新时间：2026-06-23

- 跨接收机场景下的射频指纹聚类方法
- Radio Frequency Fingerprint Clustering Method in Cross-Receiver Scenarios
- 电信科学 2026年
- 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：
  
  刘雪武（1999-），男，杭州电子科技大学硕士研究生，主要研究方向为辐射源个体识别、迁移学习。
  骆振兴（1983-），男，中国电子科技集团公司第 36 研究所高级工程师、硕士生导师，主要从事通信信号识别等方面研究。
  尚俊娜（1979-），女，博士，杭州电子科技大学教授、硕士生导师，主要研究方向为卫星通信、导航与定位方面。
- 基金信息：
  
  浙江省“十四五”第二批研究生省级教学改革项目(JGCG2024202)
- DOI：10.11959/j.issn.1000-0801.DXKX260093
  中图分类号： TN92
- 收稿：2026-02-06，
  
  修回：2026-04-29，
  
  录用：2026-06-23，
- 稿件说明：
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刘雪武, 骆振兴, 尚俊娜. 跨接收机场景下的射频指纹聚类方法[J/OL]. 电信科学, 2026.

LIU Xuewu, LUO Zhenxing, SHANG Junna. Radio Frequency Fingerprint Clustering Method in Cross-Receiver Scenarios[J/OL]. Telecommunications Science, 2026.
刘雪武, 骆振兴, 尚俊娜. 跨接收机场景下的射频指纹聚类方法[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260093.

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

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