基于凸联合的Krylov子空间自适应LMS算法

提出了一种基于凸联合的Krylov子空间自适应最小均方（least mean square, LMS）算法。首先采用Krylov子空间变换将未知系统的冲击响应转换为Krylov子空间下的稀疏结构，利用其稀疏特性，将一种改进的比例归一化LMS（improved proportionate normalized LMS, IPNLMS）算法和一种变阶数归一化LMS（variable tap length normalized LMS, VTNLMS）算法进行凸联合，最后通过Krylov子空间反变换得到未知系统冲击响应。仿真结果验证了所提出的凸联合自适应LMS算法具有更快的收敛速度和更小的稳态误差。

Adaptive LMS algorithm of Krylov subspace based on convex combination

To improve the performance of the least mean square (LMS) algorithm, an adaptive LMS algorithm of Krylov subspace based on convex combination is proposed. In this approach, the Krylov subspace transform is firstly performed to obtain the sparse structure of the unknown system impulse response in the Krylov subspace domain, and then an improved proportionate normalized LMS (IPNLMS) algorithm and a variable tap length normalized LMS (VTNLMS) algorithm are combined. Finally, the opposite Krylov subspace transform are performed to obtain unknown system impulse response. Simulation results show both the fast convergence rate and the small steady state mean square deviation (MSD) of the proposed algorithm.