【摘要】 为解决卷积混合盲信号分离时域方法收敛速度慢的问题,提出了卷积情况下的非线性主分量分析(PCA)准则,并分析其与高阶统计量准则之间的等价关系,推导了一种解决卷积盲信号分离问题的非线性PCA方法。作为一种递推最小二乘(RLS)类型的算法,所提方法与现有的自然梯度算法和高阶统计量算法相比具有收敛速度快、跟踪性能好的优点,计算机仿真实验验证了算法的有效性。更多还原

【Abstract】 A nonlinear principal component analysis(PCA) method was developed to speed up the slow convergence rate of convolutive blind source separation methods.The method is equivalent to higher-order statistical methods for some conditions.The algorithm is a recursive least-squares method which has better convergence and tracking ability than the natural gradient method and the higher-order statistical method.Simulation results illustrate the algorithm efficiency.更多还原