极点均值型经验模式分解及其去噪应用

Extrema-mean empirical mode decomposition and its application of denoising

使用经验模式分解(EMD)对信号进行去噪时,由于EMD本身会产生模态混叠,往往很难将噪声完全分离。针对这一问题,提出了一种新型的极点均值型EMD方法,并且给予固有模态函数(IMF)一个新的定义。首先,将相邻极点平均以求得均值包络,然后迭代相减进而获得IMF。最后用原始信号减去分离出的高频IMF实现去噪。随机信号仿真以及激光雷达回波信号去噪实验表明,该方法与EMD分解相比,可以更好地将噪声分离,有效地抑制模态混叠,更可以极大地减小均方误差。因此,极点均值型EMD拥有很好前景。

Using empirical mode decomposition （EMD） for denoising, it＇s difficult to remove the noise from the signal because of the mode mixing in EMD. Aiming at this problem, a new method was proposed that is extrema-mean empirical mode decomposition （Extrema-mean EMD）, making a new definition of intrinsic mode function （IMF）. Firstly, the average of each two successive extrema was calculated and the mean curve was obtained. Secondly, the mean curve was subtracted from the signal iteratively, and then the IMF was got. Finally, the high frequence IMF were removed from the original signal for denoising. Through the random signal simulation and lidar return signals de-noising experiment, it＇s turned out that compared with the EMD, the Extrema-mean EMD could restrain the mode mixing and remove the noise from the signal effectively. This method can decrease the MSE of the denoised signal significantly. Therefore, the Extrema-mean EMD has a promising future.