摘要
针对在传统辐射源个体识别方法中OFDM辐射源细微指纹特征信息会受到数据信号成分和信道噪声的影响会导致分类识别率低的问题,根据短导码的子载波频谱特点设计了一种基于固定频率边界的经验小波变换(frequency fixed boundary-empirical wavelet transform,FFB-EWT)和深度残差网络的OFDM辐射源个体识别方法.首先,提取OFDM信号的短导码,根据短导码中传输信号子载波的频率间隔划分固定边界条件,将频域边界值应用于FFB-EWT对信号进行分解,去除包含前导序列信息的子载波分量;其次,对相邻帧中包含指纹特征的空子载波分量进行积累,提高指纹特征信号的信噪比;然后,使用双通道的结合了非局部注意力模块和通道注意力模块的ResNet18残差网络,对IQ两路数据输入进行特征提取,通过Softmax函数进行分类;最后,选择Oracle公开数据集验证方法的可行性.实验结果表明利用FFB-EWT方法对6个不同辐射源个体在6 dB和0 dB条件下进行识别,准确率可以达到98.17%和89.33%,证明了该方法在低信噪比条件下的有效性.
This study proposes a novel identification method for OFDM emitters to address the issue of low classification accuracy in traditional methods for specific emitter identification,where subtle fingerprint features of OFDM emitters are affected by data signal components and channel noise.Considering the subcarrier spectrum of the short preamble,this method utilizes the fixed frequency boundary-based empirical wavelet transform(FFB-EWT)and a deep residual network.Initially,the short preamble of OFDM signals is extracted to define fixed boundary conditions based on the frequency intervals of the subcarriers in the short preamble.The boundary values in the frequency domain are then applied to FFB-EWT for signal decomposition to remove the subcarrier components containing preamble information.Subsequently,the signal-to-noise ratio of fingerprint features is enhanced by accumulating the null subcarrier components of adjacent frames.Next,a dual-channel residual network called ResNet18,integrated with a non-local attention module and a channel attention module,is used for feature extraction from IQ data inputs,with classification performed via the Softmax function.Finally,the Oracle public dataset is chosen to validate the feasibility of the method.Experimental results demonstrate that the FFB-EWT method achieves accuracy rates of 98.17% and 89.33% for identifying six different emitters under 6 dB and 0 dB conditions,respectively,proving the effectiveness of the method in environments with low signal-to-noise ratios.
作者
刘高辉
李瑞琛
LIU Gao-Hui;LI Rui-Chen(School of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China)
出处
《计算机系统应用》
2024年第9期226-234,共9页
Computer Systems & Applications
基金
国家自然科学基金(61671375)。
关键词
辐射源个体识别
固定频率边界
经验小波变换
残差网络
specific emitter identification
fixed frequency boundary
empirical wavelet transform
residual network
