Error Vector Normalized Adaptive Algorithm Applied to Adaptive Noise Canceller and System Identification

Abstract

Problem statement: This study introduced a variable step-size Least Mean-Square (LMS) algorithm in which the step-size is dependent on the Euclidian vector norm of the system output error. The error vector includes the last L values of the error, where L is a parameter to be chosen properly together with other parameters in the proposed algorithm to achieve a trade-off between speed of convergence and misadjustment. Approach: The performance of the algorithm was analyzed, simulated and compared to the Normalized LMS (NLMS) algorithm in several input environments. Results: Computer simulation results demonstrated substantial improvements in the speed of convergence of the proposed algorithms over other algorithms in stationary environments for the same small level of misadjustment. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance. Conclusion: For nonstationary environments, the algorithm performs as well NLMS and other variable step-size algorithms.