基于小波包混合优化的欠定盲源分离方法

Underdetermined blind source separation method based on wavelet packet hybrid optimization

为解决欠定盲源分离问题,提出一种基于小波包混合优化的欠定盲源分离方法。该方法采用小波包变换将观测信号分解,将观测信号的维数进行扩展,利用互相关系数值剔除冗余的信号分量,欠定盲源分离问题得到转化。接着使用贝叶斯信息准则下的奇异值分解方法估计源信号数目,通过白化过程对信号降维。最后,引入鲸鱼优化算法中的螺旋泡网狩猎行为与莱维飞行策略,对灰狼优化算法进行改进,将改进后的混合灰狼优化算法与独立成分分析算法相结合,实现重构正定白化信号的分离,从而得到源信号的近似估计。通过仿真实验对算法性能进行测试,结果验证所提方法的可行性和有效性。

To solve the problem of underdetermined blind source separation(UBSS),an UBSS method based on wavelet packet hybrid optimization is presented.The method,adopting wavelet packet transform,decomposes the observed signal,expands the dimension of the observed signal,and removes redundant signal components with the cross-correlation value,transforming the problem of UBSS.Then,with the singular value decomposition method under the Bayesian information criterion,the number of source signals is estimated,and the signal dimension is reduced through the whitening process.At last,the spiral bubble net hunting behavior and levy flight strategy in the whale optimization algorithm(WOA)are introduced to improve the gray wolf optimization algorithm,and the improved hybrid gray wolf optimization algorithm is integrated with the independent component analysis algorithm to separate the reconstructed positive definite whitening signals,and rewarding separation performance is achieved.The performance of the algorithm is tested by simulation experiments,and the results show the feasibility and effectiveness of the proposed method.