基于广义特征值和核函数的非线性盲分离算法

在给出了一种基于广义特征值分解的线性混合信号盲分离方法的基础上,结合核特征空间而给出了一种非线性混合信号盲分离算法。该算法首先将混合信号映射到高维核特征空间,同时构造该核特征空间的一组正交基。其次,通过这组正交基将高维核特征空间的信号映射到参数空间,从而把非线性混合信号盲分离问题转化为参数空间的线性混合信号盲分离问题。在参数空间中,应用基于广义特征值分解的线性混合信号盲分离方法对信号进行分离。该算法具有闭解形式,计算简单,收敛精度较高,稳定性好。仿真结果表明该算法是有效的,具有良好的分离性能。

Algorithm for nonlinear blind source separation based on generalized eigenvalue decomposition and kernel feature space

A linear blind source separation algorithm based on generalized eigenvalue decomposition is presented.Then a nonlinear blind source separation algorithm is proposed by extending the linear blind source separation algorithm to the nonlinear domain.The received mixing signals are first mapped to high-dimensional kernel feature space,and an orthonormal basis of the kernel feature space is constructed.Next,in the kernel feature space,the mixing signals are parameterized by the orthonormal basis.Finally,the linear blind source separation algorithm based on signal variability is applied to the parameterized mixing signals.The proposed algorithm has a closed-form solution and simple computation,and is characterized by high accuracy,and robustness.Simulation results illustrate the efficiency and good performance of the algorithm.