一种基于NPCA的自适应变步长盲源分离算法

Adaptive step-size blind source separation algorithm based on nonlinear principal component analysis

收敛速度和稳定误差是在线盲源分离算法的两个重要的性能指标。为了加快算法的收敛速度,提高算法的跟踪性能,提出一种基于NPCA的自适应变步长盲源分离算法。该算法的迭代步长随着输入信号和混合矩阵的变化而变化,因而具有更好的跟踪性能。仿真结果表明,该算法提高了NPCA算法的收敛速度和跟踪性能。

It is well known that the convergence rate and steady-error are crucial performance indexes for sequential Blind Source Separation（BSS） algorithms. In order to speed up the convergence rate and improve tracking ability, it proposes a novel adaptive step-size BSS algorithm based on Nonlinear Principal Component Analysis（NPCA）. The proposed algorithm utilizes an adaptive step-size whose value is adjusted in sympathy with the time-varying dynamics of the input signals and the separating matrix. Simulation results show that the proposed algorithm has faster convergence rate and better tracking ability compared with existed NPCA algorithm.