一种非平稳环境下的自适应变步长盲源分离算法

提出一种基于不完整自然梯度的变步长约束算法,用来处理非平稳环境下的瞬时盲源分离问题.该算法利用系统上的扰动对代价函数进行约束,对算法中的约束因子采用自适应形式,根据分离情况对约束因子进行自适应调整,以加快收敛速度.同时,引入基于代价函数梯度的变步长,使其具有更好的跟踪性能.仿真结果表明,在非平稳环境下,所提出的算法在提高收敛速度的同时可以有效分离源信号而不产生严重的稳态误差.

An adaptive variable step-size blind source separation algorithm in non-stationary environment

A new constrained algorithm based on the non-holonomic natural gradient with variable step-size is proposed, which can deal with the instantaneous blind source separation problem in the non-stationary environments. In order to improve the convergence speed, an adaptive means is adopted to the constrain factor, which can do the adjustment adaptively according to the separation situation. At the same time, the variable step-size based on the gradient of cost function is also applied, so that the proposed algorithm has better tracking performance. The computer simulation results show that the proposed algorithm improves the convergence speed as well as separates source signals effectively without producing serious steady state error in the non-stationary environment.