【摘要】 经验模态分解(EMD)是近年来兴起的一种非平稳信号处理方法，但在分解过程中因产生端点效应而造成分解结果严重失真。因此，研究了一种自适应波形匹配延拓法的改进方法，该方法根据信号端部不同特点确定延拓点并进行延拓，从而解决端点效应问题。实验结果表明，改进的自适应波形匹配延拓法能避免信号EMD分解失真。将改进的自适应波形匹配延拓法的EMD结合小波软阈值去噪方法用于处理加噪超声仿真信号，得到的信噪比比小波软阈值法处理后的信噪比高33.78%,说明该方法在非线性、非平稳信号处理方面有一定优势。更多还原

【Abstract】 Empirical Mode Decomposition(EMD) is a new method of non-stationary signal processing method starting to prosper in recent years, but the end effect caused by the decomposition results in serious distortion. Therefore, an improved self-adaptive waveform matching extension method is proposed to study end effect of EMD, which can determine extension points according to different characteristics of signal ends to solve the end effect problem. Experiment results show that the improved self-adaptive waveform matching extension method can avoid the distortion of EMD of signal. The improved EMD combined with wavelet soft threshold denoising method is used to process the ultrasonic simulation signal with noise, and the signal-to-noise ratio is 33.78% higher than that of the wavelet soft threshold method, indicating that this method has certain advantages in nonlinear and non-stationary signal processing.更多还原