Improving Convergence of the MPNLMS Algorithm for Echo Cancellation

摘要：

Recently, μ-law proportionate normalized least mean-square algorithm (MPNLMS) has been proposed. This algorithm exploits an approximation of the optimal proportionate step size to keep the fast initial convergence speed during the whole adaptation process until the adaptive filter reaches its steady state. However, the convergence performance of MPNLMS demonstrates slow convergence speed when the excitation signal is colored. The affine projection algorithm (APA) achieves a fast convergence speed for correlated input signals by updating the weight vector based on several previous input vectors. In this paper, generalization of the reliable method from the affine projection algorithm to a MPNLMS algorithm is presented. The proposed algorithm is evaluated using impulse responses with various degrees of sparseness. Simulations show good results in terms of speed of convergence and final mean-squared error.

关键词： proportionatealgorithms echo cancellation affine projection

作者: Li Xu Yongfeng Ju

作者单位: School of Information Engineering Changan University Xian, China School of Electronic and Control Engineering Changan University Xian, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)

会议地点: 哈尔滨

会议语种:英文

页码: 198-201

在线出版日期: 2011-01-18（万方平台首次上网日期，不代表论文的发表时间）