基于仿射投影-非线性主分量分析的盲源分离

Blind Source Separation Based on Affine Projection and Nonlinear Principal Component Analysis

仿射投影算法(APA)重复利用数据,可提高算法的收敛速度。针对现有盲源分离收敛速度慢的问题,以盲源分离的非线性主分量分析(PCA)为基础,结合仿射投影算法,提出了盲源分离的非线性APA-PCA准则,并设计出盲源分离的APA-Kalman,APA-RLS,APA-LMS新算法。在这些新算法中,预白化后的观测向量数据被重复利用,向量式数据转变成矩阵式数据,从而加快了盲源分离的收敛速度。仿真结果表明,非线性APA-PCA准则是有效的。

The affine projection algorithm(APA)can improve the algorithm convergence speed by repeated using the data.Aiming at the problem of slow convergence in the existing blind source separation(BSS),based on the nonlinear principal component analysis(PCA)for BSS,this paper proposed a nonlinear APA-PCA criterion by using the idea of APA,and the new APA-Kalman,APA-RLS and APA-LMS algorithms for BSS is designed.In these new algorithms,the prewhitened observation vector data is utilized in a repeated fashion,and the vector data is thus converted into matrix data.The convergence rate of BSS is accelerated.The simulation results show that the nonlinear APA-PCA criterion is effective and universal.