噪声环境中的EMD改进算法

经验模式分解(Empirical Mode Decomposition,EMD)是近年来出现的一种自适应的信号分解算法,该方法受到了广泛的关注,被成功应用于许多领域。然而,当信号包含噪声时,它存在过度分解的弊端,容易受噪声的干扰,因而严重地限制了该方法的推广。为了解决这一问题,提出了一种改进的EMD方法:在首轮分解时,采用光滑样条拟合来代替原来的三次样条插值,可避免对噪声成分过度分解,从而极大地减少了噪声成分的干扰。仿真实验证实了新方法有显著的改进效果;两个实际气候数据序列分解的例子进一步说明了新方法的有效性和优越性。

Improved EMD Method for Noisy Signal

Recently, an adaptive method called Empirical mode decomposition (EMD) is proposed for signal analysis. It has attracted great deal of attention and been used in many areas successfully since its advent. However, when the signal is contaminated by noise, EMD suffers from the drawback of over decomposition and likely is affected by noise, which severely restricts its applications. In order to solve this problem, an improved version of EMD is proposed. During the first decomposition circle, the original cubic spline interpolation is replaced by the smoothing spline fitting, which can avoid the over decomposition problem and then reduce the disturbance of noise component. Simulations validate the improvement of the new proposed method. Moreover, two real climate data examples show the effective and superiority of the new method for real signals.