摘要
多信道估计时,如果利用信道的稀疏性和多信道的相关性,可以提高信道估计性能。本文利用阵列信道的结构性稀疏特性,提出了一种多路分组稀疏LMS算法(Group Sparse LMS,GS-LMS)。该算法将多路信道作为一个整体同时进行自适应信道估计,通过引入l2,1范数,将结构性稀疏先验引入到稀疏LMS算法的代价函数中,导出新的滤波器权系数更新公式。仿真结果表明了在不同信道条件下,本文算法的稳态误差性能明显优于若干现有的稀疏LMS算法。
When we do muhichannel estimation,if the sparsity and relevance of multichannel are used, the performance of channel estimation can be improved. This paper proposes a Group Spame LMS algorithm which is based on structural sparse priori of the array channel. The algorithm takes these channels as a whole for adaptive channel estimation, a gradient descent recursion of the filter coefficient vector is deduced through introducing l2.1norm, which introduce structural sparse priori to the criterion function of sparse LMS algorithm. The simulation results show that, under different path conditions, steady state error performance of the proposed algorithm is obviously better than several existing sparse LMS algorithms.
出处
《信号处理》
CSCD
北大核心
2015年第8期995-1003,共9页
Journal of Signal Processing
基金
国家自然科学基金资助项目(61401315)
