联合应用MUSIC与FastICA算法实现多个时空混叠源信号的波形重建

Waveform Reconstruction of Multiple Spatio-temporal Mixed Sources by the Joint Use of MUSIC and FastICA

联合应用多信号分类与快速独立分量分析算法,分离多个时空混叠源信号,并重建其波形。利用多信号分类的方法,基于二阶统计量辨识观测信号的噪声子空间,并搜索与噪声子空间和方向矢量同时正交的多源位置参数,实现源信号波达方向的估计。利用基于固定点迭代的快速独立分量分析方法,通过最小化互信息这一高阶统计量测度来估计传感器阵列的增益模式,进而估计未知的源信号混叠矩阵。实现多个时空混叠源信号的分离与波形的重建。试验结果表明,基于多信号分类与快速独立分量分析联合的新方法,能有效辨识复值时空混叠矩阵,正确分离并重建来自不同方向的混叠源信号,从而为后续的进一步应用(如弱信号检测、故障诊断等)奠定基础。

Multiple signal classification （MUSIC） and fast independent component analysis are jointly applied to separation and waveform reconstruction of multiple spatio-temporal source signals. The MUSIC is used for identifying the noise subspace in an observation with second order statistics and search for multisource location parameters that orthogonalize the steering vector and the noise subspace. Thus, directions of arrival of multiple sources are estimated. The FastlCA based on fixed-point iteration is used for estimating the sensor gain pattern and the unknown mixing matrix of source signals, by minimizing the mutual information, a measure constructed by all statistics higher than second order. Multiple unknown spatio-temporal sources are separated and their waveforms are reconstructed. Experimental results indicate that the newly proposed method enables effective identification of complex valued spatio-temporal mixing matrix, precise separation and reconstruction of mixed sources from different directions, and establishing a solid base for some further applications such as weak signal detection and feature extraction in fault diagnosis.