基于WKGV-KICA 的盲源信号分离算法

基于核学习的非线性映射能力,提出一种小波核广义方差的核独立成分分析算法WKGV-KICA.小波核函数具有近似正交,适用于信号局部分析的优点.与互信息相联系,将核广义方差作为对比函数对统计独立性进行衡量,可以获得理想的数学特性.将该算法应用于宽范围的盲源分离问题的实例中,并与现有算法进了比较.实验结果表明, WKGV-KICA算法在同等条件下的分离精度更高,而且性能更好.

Blind source separation algorithm based on WKGV-KICA algorithm

Based on the nonlinear mapping ability of kernel learning, an algorithm of kernel independent component
analysis based on wavelet kernel generalized variance(WKGV-KICA) is proposed. The wavelet kernel which is characterized
by approximate orthogonality has the advantage in local signal analysis. Related to mutual information theory, the contrast
function defined by kernel generalized variance(KGV) has desirable mathematical properties as the measure of statistical
independence. The algorithm is applied to wide-ranging blind source separation problems and compared with existing
algorithms. Experimental results show that WKGV-KICA algorithm can achieve higher separation accuracy and better
properties under the same condition.