Radio Frequency Fingerprinting (RFF) has evolved from an early idea for radar emitter identification into a broad research field for wireless device identification and spectrum monitoring for security. Rather than presenting a conventional literature survey, this work provides a critical historical analysis of RFF organized around the field's major conceptual paradigm shifts from 1993 to 2026. We discuss the evolution of RFF across its fundamental methodological phases, beginning with early transient-based approaches, in which transmitter turn-on behavior, unintentional modulation, and hardware nonlinearities were treated as the primary fingerprint sources. We then examine the transition to digital communications, during which attention shifted to steady-state impairments and to engineered features extracted from signals. Next, we discuss the Machine Learning period, which standardized the RFF workflow around feature extraction, dimensionality reduction, and supervised classification, followed by the Deep Learning period, in which representation learning from raw IQ samples significantly improved performance and expanded the application space. Beyond a chronological list of methods and best practices, this paper critically examines the changing assumptions and persistent limitations that have driven these transitions. We highlight the central challenges that continue to shape the field, including channel dependence, receiver sensitivity, limited dataset realism, poor cross-domain generalization, open-set recognition, and adversarial robustness. By organizing more than three decades of work into a coherent narrative, this paper clarifies the evolution of RFF, identifies persistent limitations, and outlines the key research directions required to move the field toward dependable real-world adoption.

翻译：射频指纹（RFF）已从早期用于雷达发射器识别的一个概念，发展为一个涵盖无线设备识别与安全频谱监测的广泛研究领域。本文并非传统文献综述，而是围绕该领域1993年至2026年间的主要概念范式转变，提供批判性的历史分析。我们探讨了RFF在其基本方法论阶段的演变：始于早期基于瞬态的方法——此阶段发射器开机行为、非故意调制及硬件非线性被视作主要指纹源；继而考察向数字通信的过渡阶段——此时关注点转向稳态损伤及从信号中提取的工程特征；随后讨论机器学习时期——该阶段将特征提取、降维与监督分类标准化为RFF工作流程；最后阐述深度学习时期——此时基于原始IQ样本的表征学习显著提升了性能并拓展了应用空间。本文超越方法与实践的简单时序罗列，批判性地审视了推动这些转变的潜在假设变化与持续存在的局限。我们重点剖析了仍在塑造该领域的核心挑战，包括信道依赖性、接收灵敏度、数据集真实性不足、跨域泛化能力薄弱、开集识别及对抗鲁棒性。通过将三十余年的研究整合为连贯叙事，本文厘清了RFF的演变脉络，识别了持久性局限，并勾勒出推动该领域走向可靠实际部署所需的关键研究方向。