面向射频指纹信号分析与智能识别的研究综述

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中图分类号：TN919 文献标志码：A 文章编号：1673-2340（2025）02-0001-21

引文格式：，等.面向射频指纹信号分析与智能识别的研究综述[J].南通大学学报（自然科学版），2025，24（2）：1-21.

Abstract： In the context of next-generation wireless communications and multi-source heterogeneous network systems，traditional cryptographic mechanisms and security protocolsposesignificantrisks in Interet of things （IoT）environments.There isanurgent demand for more efficientandreliable identityauthentication technologies.Radio frequency fingerprinting identification （RFFI），which leverages the inherent signal characteristicsof wireless devices，providesanovel approach toaddressing device authenticationand securitychalenges.Unlike existing reviews that focus onselected aspectsof RFFI froma broad perspective，this paper proposes asystematic and comprehensive framework. It beginsbyexplaining thefundamental principlesand characteristicsof radio frequency fingerprint （RFF）.Then，from the perspectivesof statistical featuresand deep learning （DL）-based features，the paper presents an in-depthreviewof RFFI clasification and identification methods，along with a comparative analysis of the two approaches supported by experimentalvalidation.Finally，severalpotetialesearchdiections inintellgentRFFaediscussed，ndfuureteds ofRFFtechnologyareexplored，aimingtoofferboththeoreticalinsightsandpracticalguidanceforongoingresearch andreal-worldapplications.

Keywords：radio frequency fingerprint；specificemitteridentification;deeplearning；featureextraction;Internetof things;wireless communications; intelligent identification

截至2020年，全球联网的物联网设备数量已超过217亿台，预计到2025年将增长至412亿台。（剩余36925字）