摘要
为提高双滤波器结构(Dual filter structure,DFS)一级滤波器W1(k)的收敛速度,本文提出一种改进的Haar子带变换(Partial Haar transform,PHT)算法。新算法先对W1(k)的输入信号进行PHT变换以压缩滤波器长度;然后通过优化收敛步长使后验误差最小化以提高收敛速度;最后通过分时保存、维护算法的归一化因子以降低算法计算复杂度。通过提高W1(k)的收敛速度,新算法可以更少的迭代次数获得稳定的延时估计,从而提高DFS的整体收敛速度。以回声消除为应用背景对新算法进行实验仿真,实验结果表明新算法性能显著优于其他传统的自适应算法。
An improved partial Haar transform(PHT)algorithm is proposed in this paper to improve the convergence of the first filter W1(k)in the dual filter structure(DFS).In the new algorithm,the W1(k)adapts using a PHT version of the input signal to decrease its length.The convergence of W1(k)is further improved by optimizing the step size to minimize the a posteriori error.Finally,the normalized factor of the algorithm is calculated and maintained piecewisely to save computation.By increasing the convergence of the W1(k),the proposed algorithm requires less adaptations to achieve a delay estimation of the adaptive system,and the overall convergence of the DFS is eventually improved with the proposed algorithm.The simulation results in the context of echo cancellation indicate that compared with other traditional adaptive algorithms,the proposed algorithm is found to be more efficient in sparse system identification.
作者
文昊翔
洪远泉
罗欢
WEN Haoxiang;HONG Yuanquan;LUO Huan(College of Intelligent Engineering,Shaoguan University,Shaoguan,512000,China)
出处
《数据采集与处理》
CSCD
北大核心
2020年第6期1174-1181,共8页
Journal of Data Acquisition and Processing
基金
广东省教育厅青年创新人才基金(2016KQNCX156)资助项目。
关键词
稀疏系统辨识
双滤波器结构
自适应算法
HAAR变换
延时估计
sparse system identification
dual filter structure
adaptive algorithm
Haar transform
delay estimate
