针对原始RLS类算法无法用于超定和源信号数目动态变化的盲分离问题,本文采用一种新的在线估计源信号数目的方法.通过在线估计观测信号均值和协方差矩阵,定义一个关于源信号数目的代价函数,然后最小化代价函数可得到源信号数目的估计.并且利用估计得到的源数目动态调整RLS算法中的分离矩阵及其它相关参数矩阵的维数,进而使得改进RLS盲分离算法能够有效地分离超定和数目动态变化的源信号.仿真结果表明,新的算法比现有算法具有更好的收敛性和分离性能.%Since the RLS-like algorithm can not be used in the super-condition and dynamic source numbers' blind source separation problem, we introduce a novel RLS algorithm combing with an online source number estimation method. First it defines a cost function related to the source number based on estimation of the mean value and the covariance matrix of the observed signals, then minimizes the function to achieve the estimation of the source number. The dimensions of the separated matrix and other related parameter matrixes can be dynamically modified according to the estimated source number, which makes the RLS-like algorithm able to separate the source signals efficiently in the super-condition and dynamic source numbers environments. Computer simulations show that the new algorithms is better than the existing algorithm with faster convergence and better separation results.
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