基于FEEMD算法对小样本电磁信号的识别与分类  

Recognition and classification of small sample electromagnetic signals based on FEEMD algorithm

作　　者：郭钰荣 姚金杰[1] 白建胜 温雪芳 Guo Yurong;Yao Jinjie;Bai Jiansheng;Wen Xuefang(North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学,太原030051

出　　处：《国外电子测量技术》2023年第4期166-172,共7页Foreign Electronic Measurement Technology

基　　金：山西省基础研究计划项目(202203021212157);国家自然科学基金(62201522)项目资助。

摘　　要：针对当前小样本条件下电磁信号识别算法在不同信噪比下识别准确率较低的问题,提出了一种模糊熵限阈经验模态分解(fuzzy entropy empirical mode decomposition,FEEMD)算法进行电磁信号特征提取,提取表征明显的数据展开短时傅里叶变换(short time Fourier transform,STFT),然后选用Transformer模型分类识别各制式信号。该算法采用8种不同制式的电磁信号分别在-10、-5、0、5、10 dB这5种信噪比下的识别准确率,确定了该网络的最优超参数。仿真结果表明,在5种信噪比下,2FSK、AM、ASK、SSB这4种调制信号识别率均超过90%,QAM16、QPSK和OFDM的准确率由30%~40%提升到了70%以上,由此表明了该算法的有效性和可实施性。Aiming at the problem that the identification accuracy of current small sample electromagnetic signal identification algorithm is low under different SNR,a fuzzy entropy empirical mode decomposition(FEEMD)algorithm is proposed to extract electromagnetic signal feature.Extract the data with obvious characterization to expand short-time Fourier transform(STFT),and then select Transformer model to classify and identify each standard signal.The algorithm uses the recognition accuracy of 8 kinds of communication signals under the five SNR of-10,-5,0,5 and 10 dB respectively to determine the optimal hyperparameters of the network.Simulation results show that under the five SNR,the recognition rate of the four modulated signals(2FSK,AM,ASK and SSB)is more than 90%,and the accuracy rate of QAM16,QPSK and OFDM is increased from 30%~40%to more than 70%,which shows the effectiveness and practicability of the algorithm.

关 键 词：短时傅里叶变换 Transformer网络 FEEMD算法 信噪比 

分 类 号：TN911[电子电信—通信与信息系统]