电子测量与仪器学报

周航,丁建,林川,李相强.基于二次 CEEMD 与时域特征分析的去噪方法[J].电子测量与仪器学报,2023,37(3):222-229

基于二次 CEEMD 与时域特征分析的去噪方法

Denoising method based on secondary CEEMD and time domain feature analysis

DOI：

英文关键词:empirical mode decomposition time domain feature analysis intrinsic mode function denoising

基金项目:电波环境特性及模化技术重点实验室基金(6142403200306)、四川省科技计划(2023NSFSC0463)、中国电波传播研究所稳定支持科研经费(A132003W02)项目资助

作者	单位
周航	1. 西南交通大学电气工程学院
丁建	2. 中国电波传播研究所电波环境特性及模化技术重点实验室，3. 西安电子科技大学综合业务网理论及关键技术国家重点实验室
林川	1. 西南交通大学电气工程学院
李相强	4. 西南交通大学物理科学与技术学院

Author	Institution
Zhou Hang	1. School of Electrical Engineering, Southwest Jiaotong University
Ding Jian	2. National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation,3. The State Key Laboratory of Integrated Services Network, Xidian University
Lin Chuan	1. School of Electrical Engineering, Southwest Jiaotong University
Li Xiangqiang	4. School of Physical Science and Technology,Southwest Jiaotong University

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中文摘要:

为克服经验模态分解(EMD)去噪方法存在的模态混叠以及噪声分量与信号分量区分困难问题,本文提出了一种基于二次互补集合经验模态分解(CEEMD)与时域特征分析的去噪方法。该方法利用 CEEMD 来克服模态混叠问题,同时基于对 CEEMD 本征模态函数(IMF)的时域特征分析来确定噪声主导 IMF 分量与信号主导 IMF 分量的分界点,据此区分噪声分量与信号分量,并对分界点相邻两侧的噪声主导 IMF 分量与信号主导 IMF 分量进行二次 CEEMD 分解,在保留更多有用信号的同时进一步滤除剩余噪声。对含冲击噪声干扰的实际机载平台数据的去噪实验结果表明,新方法通过对噪声分量与信号分量的有效分离,可以更好地抑制噪声干扰,明显提升信噪比。

英文摘要:

In order to solve the mode-mixing problem of empirical mode decomposition (EMD) and overcome the difficulty of separating the noise components and signal components, a novel denoising method based on secondary complementary ensemble empirical mode decomposition (CEEMD) and time domain feature analysis is presented in this paper. In the proposed method, CEEMD is employed to solve the mode-mixing problem, then the boundary of noise dominant intrinsic mode function (IMF) components and the signal dominant IMF components is determined based on the time domain feature analysis of IMFs returned by CEEMD, whereby the noise components and the signal components are distinguished. Secondary CEEMD decomposition is performed on the noise dominant IMF component and signal dominant IMF component at the boundary to further filter the residual noise while maintain as much useful signal as possible. The experimental results of denoising the actual airborne platform data with impulse noise interference show that the proposed method can better suppress the noise interference and significantly improve the signal-to-noise ratio by effectively separating the noise and signal components.

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