基于希尔伯特-黄变换的自适应相位提取法  

Adaptive Phase Extraction Method Based on Hilbert-Huang Transform

作　　者：王辰星[1] 达飞鹏[1] 

机构地区：[1]东南大学自动化学院,江苏南京210096

出　　处：《光学学报》2012年第11期126-136,共11页Acta Optica Sinica

基　　金：国家自然科学基金(51175081;61107001);江苏省自然科学基金(BK2010058);东南大学优秀博士论文基金资助课题

摘　　要：提出了一种基于希尔伯特-黄变换的自适应相位提取法。该方法通过对条纹图信号进行经验模态分解得到一系列本征模函数(IMF)。对每个IMF进行希尔伯特谱分析,提出准则用以确定噪声IMF并判断是否存在模式混叠问题。若存在,根据该噪声IMF自适应设计新的"噪声"并将其添加到原信号中,然后对所形成的新信号再次分解,重复进行该过程直到相应的模式混叠问题不再存在。将最后一次分解所得的噪声IMF和背景分量从信号中去除,对所得的基频分量做希尔伯特变换即可得到条纹图的包裹相位分布。所提方法可有效克服模式混叠问题,可在有效去除噪声和背景分量的同时尽量保留细节相位信息,有较好的自适应性及稳健性,测量精度高。Based on Hilbert-Huang transform, an adaptive phase extraction method is proposed. Signals of fringe pattern are decomposed into a series of intrinsic mode functions （IMF） by empirical mode decomposition. Criteria are presented to determine noise IMF and to identify whether mode-mixing problem exists in the corresponding noise IMF through the analysis of Hilbert spectra for each IMF. If the problem exists, new ＂noise＂ is designed adaptively according to the noise IMF and added into the original signals. The new formed signals are decomposed again. Repeat the process iteratively until the mode-mixing problem disappears. Noise IMF and background components determined in the last decomposition are subtracted from the last formed signals to get the fundamental signals, on which is performed Hilbert transform to obtain the wrapped phase. The presented method can overcome the mode-mixing problem effectively and keep detailed phase information while removing the noise and background components. The adaptability and robustness of the method is good.

关 键 词：测量 相位提取 模式混叠问题 经验模态分解 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]