An Improved Proportionate Normalized Least Mean Square Algorithm for Sparse Impulse Response Identification

In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefcient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity.

正比例扩展相关LMS算法

传统的回声消除系统无法在嘈杂的双工环境下工作。由于双工信号的干扰,误差信号不能正确地指导滤波器系数的更新。本文中提出了一种基于相关函数的自适应滤波算法,扩展型相关函数LMS(ECLMS)算法。计算机仿真结果显示E-CLMS算法的表现要优于传统LMS算法。然而,ECLMS的收敛速度仍然不能令人满意。为了提高ECLMS算法的收敛速度,本文推出了一种新的正比例步长调整算法,针对自适应滤波器系数的每一个抽头,提供一个合理的步长调整参数。计算机仿真结果证实了正比例扩展相关LMS算法理论的正确性。