In order to improve the convergence speed and steady-state error of the least mean square algorithm for gaussian noise envi-ronment and sparse system,a variable step size zero attraction normalized least mean square algorithm is proposed,which combines the improved sparse perception norm of Versoria function with the normalized least mean square algorithm,and introduces a new gaussian-like variable step size strategy,solving the problems of slow convergence and poor tracking performance under the condition of fixed step size.The convergence of the proposed algorithm is analyzed theoretically,and the influence of parameters in the improved Gaussian-like step function on the performance of the algorithm is discussed based on MATLAB platform.Finally,the proposed algorithm and other similar algorithms are applied to unknown system identification experiments in gaussian noise environment and sparse environment with different SNR conditions.Simulation results show that the proposed algorithm has faster convergence speed,better tracking ability and smaller steady-state error.
关键词
自适应滤波/最小均方算法/归一化/类高斯函数/零吸引
Key words
adaptive filtering/least mean square algorithm/the normalized/gaussian-like function/zero to attract
维普
万方数据