一种稀疏系统辨识的子带自适应滤波算法

Improved multiband-structured subband adaptive filter for sparse system identification

利用改进的多带结构子带自适应滤波(IMSAF)算法辨识具有稀疏特性的未知系统。代价函数引入加权的l_1范数作为附加约束,并结合次梯度分析方法推导出新的更新方程。根据加权矩阵选取的不同,提出了两个l_1范数约束的IMSAF算法:l_1-IMSAF和l_1-RIMSAF。仿真结果表明,在未知系统具备稀疏特性的条件下,相较于传统的IMSAF算法,两个新算法的收敛性能具有显著提高。

The improved multiband-structured subband adaptive filter （IMSAF） algorithm is used to identify unknown system with sparse characteristics. AIt -norm of the filter taps is introduced into the cost function. And then the new update e- quation is derived by using a subgradient analysis. According to the different weighting matrices, two kinds of the l^-norm IMSAF are proposed. Simulation results prove that under sparse system conditions, two proposed methods have a significant improvement in both convergence rate and steady-state misadjustment compared with the standard IMSAF algorithm.