Denoising method based on secondary CEEMD and time domain feature analysis
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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。
Abstract:
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.
