强噪声背景下的经验模式分解研究

Research on empirical mode decomposition of signals submerged in a heavy noise

研究了强噪声混合条件下微弱信号的经验模式分解(EMD)问题,提出了一种基于随机共振降噪的EMD分解方法。该方法利用随机共振在微弱信号检测方面的独特优势,首先对有噪微弱信号进行随机共振输出,微弱信号得到降噪和加强后,再进行EMD分解。在仿真实验中,对随机共振输出前后的信号分别进行EMD分解,分析结果表明该方法不仅能够提高原始信号的信噪比,有效检测出被噪声淹没的微弱信号从而提高了EMD分解的质量,同时减少了EMD分解的层数,提高了运算效率。

The empirical mode decomposition(EMD) of weak signals submerged in a heavy noise was conducted and a method of stochastic resonance(SR) used for noisy EMD was presented.This method used SR as the pretreatment of EMD to remove noise and detect weak signals.The experiment results proved that this method,compared with EMD used directly,not only improves SNR and enhances weak signals,but also improves the decomposition performance and reduces the decomposition layers of signals.