中文摘要：

为了避免单个滤波器在收敛速度与稳态误差上相互制约,从而导致系统性能降低的问题,本文采用凸组合最小均方算法（Combined Least Mean Square,CLMS）,将快速滤波器和慢速滤波器并联使用,同时为进一步改善CLMS算法的性能,对已有的变步长凸组合最小均方算法（Variable Step-size Convex Combination of LMS,VSCLMS）做出改进,提出了一种新的VSCLMS算法.在该算法中,对快速滤波器选用以最小均方权值偏差（Minimization of Mean Square Weight Error,MMSWE）为准则的按步分析的变步长滤波器;对慢速滤波器采用以稳态最小均方误差（Least Mean Square,LMS）为准则的固定步长滤波器.通过理论分析与仿真实验表明,该算法能够在噪声、时变以及非平稳的环境下保持较好的随动性能,且在各个阶段均保持良好的收敛性,与传统的CLMS、VSCLMS算法相比,不仅具有更快的收敛速度,而且拥有稳定的均方性能和较优的跟踪性能,为自适应滤波算法的研究提供了一条可行途径.

英文摘要：

In order to avoid the conflict between the convergence speed and stable state error for a single LMS filter,and degrade the performance of the recognition system,we used combined least mean square（ CLMS） algorithm which is the parallel of fast LMS filter and slow LMS filter. Meanwhile,to further improve the performance of the CLMS algorithm,a new variable step-size convex combination of LMS（ VSCLMS） algorithm was proposed by improving original VSCLMS. In the proposed algorithm,we considered the variable step-size filter on the basis of minimum mean square weight error（ MMSWE） as the fast LMS filter,and a new constant step-size filter based on steady-state LMS is used as the slow filter. By analyzing theory and experimental results,the proposed algorithm,which compared with the original VSCLMS algorithm and CLMS algorithm,not only has a superior capability of tracking under the environment of noise,time-varying and unstable condition,but also can maintain a better convergence.