Natural gradient-based recursive least-squares algorithm for adaptive blind source separation

This paper focuses on the problem of adaptive blind source separation (BSS). First, a recursive least-squares (RLS) whitening algorithm is proposed. By combining it with a natural gradient-based RLS algorithm for nonlinear principle component analysis (PCA), and using reasonable approximations, a novel RLS algorithm which can achieve BSS without additional pre-whitening of the observed mixtures is obtained. Analyses of the equilibrium points show that both of the RLS whitening algorithm and the natural gradient-based RLS algorithm for BSS have the desired convergence properties. It is also proved that the combined new RLS algorithm for BSS is equivariant and has the property of keeping the separating matrix from becoming singular. Finally, the effectiveness of the proposed algorithm is verified by extensive simulation results.

一种按块递归的盲源分离方法

自然梯度算法比随机梯度算法有更好的收敛性能和数值稳定性,块递归算法需要较少的运算时间.结合这两者的优点,提出一种基于块递归的盲源分离算法.首先基于自然梯度和非线性主分量分析,构造出按块递归更新的矩阵方程,然后用QR分解和回代法逐块求解该矩阵方程得到最优分离矩阵.与已有递归型盲源分离算法相比,数值仿真实验表明本方法运行一次所需平均时间减少了65%,所求矩阵的正交性能指标改善了10 dB.

超定盲信号分离RLS算法研究

研究在线盲信号分离问题。将适定盲信号分离的RLS算法,推广到源信号个数未知的超定盲信号分离模型中,利用正交投影方法消除了超定盲信号分离RLS算法的冗余移动,设计出能够稳定收敛的超定盲信号分离RLS算法。仿真实验验证了算法的有效性和其收敛的稳定性。