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面向新一代无线通信和多源异构网络系统，传统的密码机制和安全协议在物联网环境下存在较大的安全隐患，亟需更加高效、可靠的安全识别技术。射频指纹识别（radio frequency fingerprinting identification,RFFI）作为一种基于设备固有信号特征进行识别的技术，为解决无线设备的身份识别和安全问题提供了新的思路和手段。现有的综述大多从较为宽泛的视角对RFFI的研究现状进行分析，未能对RFFI的整体框架进行全面系统的阐述，缺乏对各个关键部分的深入总结和讨论，无法满足当前研究的系统性需求。文章基于RFFI的研究现状，提出了一个系统的整体框架，从射频指纹（radio frequency fingerprint,RFF）的基础出发，全面介绍了RFF的产生机理及特性；从统计特征和深度学习（deep learning,DL）特征2个角度出发，综述了RFF的分类及其识别方法，并对二者进行了比较分析，辅以实验验证分析；最后，重点分析了智能RFFI领域的几个潜在研究方向，并展望了RFF技术在未来的发展趋势，旨在为智能RFFI的研究与实际应用提供理论启发和实践参考。
Abstract：In the context of next-generation wireless communications and multi-source heterogeneous network systems, traditional cryptographic mechanisms and security protocols pose significant risks in Internet of things(IoT) environments. There is an urgent demand for more efficient and reliable identity authentication technologies. Radio frequency fingerprinting identification(RFFI), which leverages the inherent signal characteristics of wireless devices, provides a novel approach to addressing device authentication and security challenges. Unlike existing reviews that focus on selected aspects of RFFI from a broad perspective, this paper proposes a systematic and comprehensive framework.It begins by explaining the fundamental principles and characteristics of radio frequency fingerprint(RFF). Then, from the perspectives of statistical features and deep learning(DL)-based features, the paper presents an in-depth review of RFFI classification and identification methods, along with a comparative analysis of the two approaches supported by experimental validation. Finally, several potential research directions in intelligent RFFI are discussed, and future trends of RFF technology are explored, aiming to offer both theoretical insights and practical guidance for ongoing research and real-world applications.
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基本信息:
DOI：10.12194/j.ntu.20250105001
中图分类号:TN918.4
引用信息:
[1]闫高丽,付雪,王禹等.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(02):1-21.DOI:10.12194/j.ntu.20250105001.
基金信息:
国家自然科学基金面上基金项目(62471247)