稀疏时频表示的初始相位估计方法

A Method Evaluating the Phase of the Signal for Sparse Time Frequency Representation

  • 摘要: 经验模态分解是处理非平稳信号的常用方法之一。在本征模态函数与稀疏优化相结合的表示框架中,基于聚合经验模态分解的思想,本文提出了一种初始相位估计的方法。该方法首先通过噪声辅助的分解算法自适应地消除部分噪声,再用标准算子方法得到剩余分量的相位估计值。理论分析和仿真实验均表明该算法能够降低已有的相位估计算法对噪声的敏感程度,改善了信号稀疏时频表示方法的表示效果。

     

    Abstract: Empirical Mode Decomposition(EMD) is one of the method which is usually used to process the signal. Utilizing the idea of the Ensemble Empirical Mode Decomposition(EEMD), an adaptive phase evaluation algorithm is proposed based on the frame of Intrinsic Mode(IMF) Function combined with the sparse optimization. This algorithm uses a kind of noise assisted decomposition method to eliminate parts of noise. Then the normalization method is employed to evaluate the phase. Theoretical analyze and numerical simulation demonstrate it can persist the disturbance of noise compared with the present phase evaluation algorithm so that Sparse Time Frequency Representation(STFR) method can have a comparative better representation.

     

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