数据采集与处理2026，Vol.41Issue(3)：674-686,13.DOI:10.16337/j.1004-9037.2026.03.004
基于扰动感知的辐射源射频指纹识别方法
A Perturbation-Aware Method for Radio Frequency Fingerprint Identification of Specific Emitters
摘要
Abstract
Specific emitter identification(SEI)leverages the inherent hardware imperfections of wireless device RF front-ends to achieve device identification,serving as a crucial technology for ensuring wireless communication security.However,in complex electromagnetic environments,various perturbations such as carrier frequency offset,sampling clock deviation,gain fluctuation,time shift,and chirp modulation collectively cause distribution shifts in signal features,thereby weakening the stability of RF fingerprint representation and degrading identification performance.To address these issues,this paper proposes a perturbation-aware modulated convolutional neural network(PAM-CNN).This method first utilizes a perturbation-aware branch to jointly estimate the perturbation state and its parameters in the input signal,and then performs sample-adaptive modulation of the convolution kernels based on the estimation results.This enables the network to structurally suppress the impact of perturbations during the feature extraction process.Simultaneously,a multi-task joint training framework is constructed,incorporating device identification,perturbation detection,and parameter regression,to enhance the model's robust representation capability under complex perturbation conditions.Experimental results on a real over-the-air ADS-B baseband dataset and its offline perturbation-augmented data demonstrate that,under various superimposed perturbation conditions,the proposed method achieves an identification accuracy of 95.39%at a signal-to-noise ratio(SNR)of 15 dB and outperforms comparison methods across the full SNR range.The results indicate that this method can effectively enhance the robustness of SEI in complex electromagnetic environments.关键词
辐射源个体识别/射频指纹/扰动感知/调制卷积网络/多任务学习Key words
specific emitter identification(SEI)/radio frequency fingerprint/perturbation awareness/modulated convolutional network/multi-task learning分类
信息技术与安全科学引用本文复制引用
赵雨露,李志刚,查浩然,韩宇,林云..基于扰动感知的辐射源射频指纹识别方法[J].数据采集与处理,2026,41(3):674-686,13.基金项目
空间智能操纵技术国家级重点实验室自主科研项目(2025-JCJQ-LC-020-24). Independent Research Project of the National Key Laboratory of Space Intelligent Manipulation Technology(No.2025-JCJQ-LC-020-24). （2025-JCJQ-LC-020-24）
