经验模式分解联合独立分量分析降噪研究

The de-noising study based on empirical mode decomposition and independent component analysis

针对高精度GPS变形监测的噪音成分以及多路径效应的剔除,提出了一种新的经验模式分解联合独立分量分析的滤波降噪法。采用模态相关准则进行信号层与噪音层的判定,有效地解决了低信噪比情况下信号层与噪音层分界点的判定,实现了噪音最大化的去除以及有用信息的最大化保留;基于经验模式分解的独立分量分析滤波降噪法,采用更为简单科学的数学判定方法,避免了人为的经验判定方法,实现了信号层与噪音层的自适应判定以及降噪效果更佳。实验结果表明:所用算法不仅能够有效地去除噪音成分,而且能够很好地保留大部分有用信息,研究结果对高精度GPS变形监测的去噪以及多路径效应的剔除研究具有一定意义。

In order to solve the problem of removing the noise and multipath effect, this paper proposed a new filter method based on empirical mode decomposition （EMD） and independent component analysis （ICA）. The EMD mode was used to distinguish the boundary of noise and useful signal and remove the maximum noise, which maximized the retention of useful signal. The EMD combined ICA filter method was easy and scientific for the signal and noise adaptive decision, and the de-noising result was pretty good. Experimental results showed that this method could not only effectively remove the noise component, but also retain the most useful information, which is meaningful to the research of denoising and GPS multipath effect extraction in high-precisione GPS deformation monitoring.