A Radio Frequency Fingerprint Identification Method Based on Multi-Feature Fusion and Multi-Task

XIONG Songlei; SONG Tianxin; SU Yuwei; ZHANG Di

doi:10.14188/j.1671-8836.2022.0233

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A Radio Frequency Fingerprint Identification Method Based on Multi-Feature Fusion and Multi-Task

更新时间：2023-11-01

- A Radio Frequency Fingerprint Identification Method Based on Multi-Feature Fusion and Multi-Task
- J Wuhan Univ （Nat Sci Ed） Vol. 69, Issue 5, Pages: 545-552(2023)
- 作者机构：
  
  1.郑州大学电气与信息工程学院，河南郑州 450001
  2.河南省网络密码技术重点实验室，河南郑州 450001
  3.天地一体化信息技术国家重点实验室，北京 100086
- 作者简介：
- 基金信息：
- DOI：10.14188/j.1671-8836.2022.0233
  CLC：
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XIONG Songlei, SONG Tianxin, SU Yuwei, et al. A Radio Frequency Fingerprint Identification Method Based on Multi-Feature Fusion and Multi-Task. [J]. J Wuhan Univ （Nat Sci Ed） 69(5):545-552(2023)
DOI：

XIONG Songlei, SONG Tianxin, SU Yuwei, et al. A Radio Frequency Fingerprint Identification Method Based on Multi-Feature Fusion and Multi-Task. [J]. J Wuhan Univ （Nat Sci Ed） 69(5):545-552(2023) DOI： 10.14188/j.1671-8836.2022.0233.

摘要

目前关于射频指纹识别（radio frequency fingerprint identification，RFFI）的研究大多基于单个信号特征，存在识别准确率不高的问题。为此，提出了一种多特征融合多任务的射频指纹识别方法。该方法融合功率谱、基于STFT变换的时频谱、互功率谱三种信号特征，采用多任务学习（multi-task learning，MTL）策略，使用噪声信息作为先验知识来帮助网络训练，以设备分类为主任务，以信号噪声含量的分类作为网络第二个任务。仿真实验表明，本文提出的多特征融合多任务的方法较单特征单任务的方法有所提高，是一种有效的射频指纹识别方法。

Abstract

Most of the current research on Radio Frequency Fingerprint Identification (RFFI) is based on individual’s single signal feature, which has the problem of low identification accuracy. To this end, this paper proposes a multi-feature fusion multi-task RF fingerprint identification method, which fuses three signals: power spectrum, time-frequency spectrum based on STFT transform, and mutual power spectrum, and uses noise information as a priori knowledge to help network training, with device classification as the main task and classification of signal noise content as the second task of the network for multi-task learning (MTL). Simulation experiments show that the multi-feature fusion multi-task approach proposed in this paper is improved over the single-feature single-task approach and is an effective method for RF fingerprint identification.

关键词

物理层安全信号处理射频指纹深度学习特征融合

Keywords

physical layer securitysignal processingradio frequency fingerprintdeep learningfeature fusion

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