New normalized LMS adaptive filter with a variable regularization factor

作     者：LI Zhoufan LI Dan XU Xinlong ZHANG Jianqiu 

作者机构：School of Information Science and TechnologyFudan University 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2019年第30卷第2期

页      面：259-269页

核心收录：

学科分类：07[理学] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China(61571131 11604055) 

主　　题：adaptive filtering normalized least mean square (NLMS) minimum-disturbance (MD) constraint variable regularization variable step-size NLMS 

摘      要：A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.