基于改进S变换和ICA的相关源分离方法

含有同频成分的机械振源信号不满足统计独立条件,无法直接采用传统盲源分离方法进行分离与识别,为解决该问题,提出了一种基于改进S变换(modified S-transform,简称MST)和独立成分分析(independent component analysis,简称ICA)的相关源分离方法。首先,通过改进S变换对观测信号进行时频化处理,利用相关成分在时频域中实部和虚部的向量夹角,识别并剔除混合信号中的相关项,保证新的观测信号满足独立性条件;其次,以负熵为独立性测度,基于快速固定点独立成分分析进行分离矩阵估计;最后,将该矩阵用于最初的观测信号,从而分离出振源信号,定量计算各个振源的贡献比。通过仿真和实例分析验证了该方法在相关性振源分离中的有效性。

Blind Separation of Correlated Sources Based on Modified S-Transform and ICA

The mechanical vibration source signals with common frequency components cannot satisfy a statistically independent condition and be directly separated by a traditional blind source separation method. In order to solve this problem, a novel correlated source separation method based on modified S-transform (MST) and independent component analysis (ICA) is proposed. First of all, the modified S-transform is used to the time-frequency preprocessing of observation signals, taking advantage of the vector angle of the components in the real and imaginary parts of the time-frequency domain, and identifying and eliminating the dependent components in the mixed signals to ensure that the new reconstruction signals satisfy the independence condition. Then, using the negative entropy as an independent measure, the separation matrix is estimated based on the fast fixed-point independent components analysis. Finally, the correlated source signals are recovered through the matrix separating the initial observation signals and quantitatively calculated the contribution ratio of each vibration source. The effectiveness of proposed method in correlated source separation is verified by simulation and experiment results.