基于人工智能模型的工业互联网射频指纹识别技术研究

Research on Industrial Internet Radio Frequency Fingerprinting Technology Based on Artificial Intelligence Model

射频指纹识别(Radio Frequency Fingerprinting,RFF)技术为工业互联网提供了关键的数据采集和处理能力,然而,现存RFF技术存在识别率低的问题。因此,以人工智能模型为基础,提出一种基于轻量化残差神经网络的RFF算法,旨在优化射频信号的特征提取和识别过程,在降低计算复杂度的同时,保持高识别性能。实验通过USRP2954设备采集信号并识别,结果显示,研究提出的方法在高信噪比环境下达到98.46%的识别率。研究的创新对工业互联网和智能制造领域具有重要意义,能够推动工业自动化和智能制造的进一步发展,提升整个工业系统的智能化水平。

Radio Frequency Fingerprinting(RFF)technology provides the industrial Internet with key data acquisi-tion and processing capabilities.However,current RFF technology faces the problem of low recognition accuracy.Therefore,based on Artificial Intelligence(AI)models,this article proposes a RFF algorithm based on lightweight residual neural networks,in order to improve the feature extraction and recognition process of radio frequency sig-nals,reducing computational complexity while maintaining high recognition accuracy.Experiments using the USRP2954 device for signal collection and recognition show that the proposed method achieves a recognition rate of 98.46%in high signal-to-noise ratio environments.The innovation of this technology is of great significance to the fields of industrial internet and intelligent manufacturing,as it helps to promote the development of industrial automation and intelligent manufacturing,further enhancing the intelligence level of the entire industrial system.