一种基于核函数的杂系盲源分离算法

Blind source separation algorithm based on kernel function to hybrid signals

提出了一种基于核函数的杂系盲源分离算法,该算法可以充分分离杂系混合信号。通过引入非线性核函数和平滑参数,将分离信号进行非线性核映射,最优化平滑参数,同时更新混合分离矩阵,通过不断迭代学习,对混合信号进行盲源分离。仿真结果表明,与EASI、白化和自然梯度算法相比,本方法能更有效地分离同系混合或杂系混合信号,收敛速度更快,且能够适应于非平稳环境,具有一定的实用性。

This paper proposed a blind source separation algorithm based on kernel function which could be used to separate the hybrid signals adequately.Firstly the proposed algorithm would be taking in a nonlinear kernel function and the smoothing parameter,and mapping the separated signals to a nonlinear kernel space at the same time,what＇s more,by optimizing the smoothing parameter and updating the mixed separation matrix step by step to separate the signals effectively.According to the simulation results,the presented algorithm shows better significantly performances both in convergence rate and steady state aspects comparing with EASI method,whitening method,natural gradient method,meanwhile,it also can be adapt in non-stationary environment,with a great importance in practical application.