间隔阈值的多元经验模态分解电磁信号去噪  

Noise Reduction in Electromagnetic Emanation Using MEMD Interval Threshold

作　　者：李端[1,2] 张洪欣[1,3] 李强[1] 

机构地区：[1]北京邮电大学电子工程学院,北京100876 [2]河南理工大学电气工程与自动化学院,焦作454000 [3]北京邮电大学北京市安全生产智能监控重点实验室,北京100876

出　　处：《环境技术》2015年第4期60-64,共5页Environmental Technology

基　　金：国家自然科学基金(61202399;61171051)

摘　　要：传统小波分解去噪需要先验知识,缺乏自适应性,将多元经验模态分解间隔阈值(Multivariate Empirical Mode Decomposition,MEMD)滤波方法用于电磁泄漏信息预处理。首先对电磁泄漏信号进行对齐处理。然后,针对多元经验模态分解直接阈值滤波产生的不连续性问题,提出采用MEMD间隔阈值滤波方法进行不相关模态滤波,解决传统经验模态分解的模态混叠问题。最后,分别采用巴特沃斯低通滤波、小波阈值、MEMD-DT和MEMD-IT方法对密码芯片电磁泄漏数据进行去噪处理,通过基于最小二乘支持向量机分类识别,实验结果表明该自适应去噪方法的优于传统的电磁侧信道分析预处理方法。To overcome the dependence of prior knowledge and non-adaptive of traditional filtering algorithm, a Multivariate Empirical Mode Decomposition interval threshold（MEMD-IT） de-noising which is a nonparametric signal de-noising approach is presented as a preprocessing stage for electromagnetic radiation signals. First, the near field electromagnetic leakage signals were captured while cipher device was executing RC4 algorithm. MEMD-IT is developed which can reduce the discontinuity induced by EMD-DT. Comparing with the other filters, such as Butterworth low-pass filter, wavelet threshold denoising, and MEMD direct threshold（MEMD-DT） de-noising, the proposed de-noising method evaluated by the Hanming weight classification results based on LSSVM has a better performance.

关 键 词：电磁侧信道分析 多元经验模态分解 间隔阈值去噪 最小二乘支持向量机(LSSVM) 

分 类 号：TN918[电子电信—通信与信息系统] TP393.08[电子电信—信息与通信工程]