一种稳健的基于峰度的独立分量分析算法

Robust independent component analysis based on kurtosis

给出了一种基于峰度最大化的独立分量分析算法,通过优化步长因子来得到全局最优值,采用代数方法求解方程根的方法得到最优步长参数,与迭代算法求解相比,结构清晰、实现简单,而且不需要对混合信号做白化预处理,算法运算量较低。仿真实验验证了算法优良特性。

A novel method for deflationary ICA,referred to as robust ICA,is put forward.This simple technique consists of performing exact line searchoptimization of the kurtosis contrast function.The step size leading to the global maximum of the contrast along the search direction is found among the roots of a fourth-degree polynomial.This polynomial rooting can be performed algebraically,and thus at low cost,at each iteration.Among other practical benefits robust ICA can avoid prewhitening and deals with mixtures of possibly noncircular sources alike.The algorithm is robust to local extrema and shows a very high convergence speed in terms of the computational cost required to reach a given source extraction quality.The simulation justifies its effectiveness.