Simulation and Performance Analysis of LMS and NLMS Adaptive Filters in Non-stationary Noisy Environment

2011 International Conference on Computational Intelligence and Communication Networks Pub Date : 2011-10-07 DOI:10.1109/CICN.2011.148

K. Borisagar, B. Sedani, G. R. Kulkarni

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引用次数: 6

Abstract

One of the most important applications of adaptive filter is Interference or noise cancellation. The objective of adaptive interference cancellation is to obtain an estimate of the interfering signal and to subtract it from the corrupted signal and hence obtain a noise free signal. The tracking performances of the LMS and NLMS algorithms are compared when the input of the adaptive filter is no stationary For this purpose, the filter uses an adaptive algorithm to change the value of the filter coefficients, so that it acquires a better approximation of the signal after each iteration. The LMS (Least Mean Square), and its variant the NLMS (Normalized LMS) are two of the adaptive algorithms widely in use. This paper presents a comparative analysis of the LMS and the NLMS in case of interference cancellation from speech signals. For each algorithm, the effects of two parameters-filter length and step size have been analyzed. Finally, the performances of the two algorithms in different cases have been compared.

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非平稳噪声环境下LMS和NLMS自适应滤波器的仿真与性能分析

自适应滤波器最重要的应用之一是消除干扰或噪声。自适应干扰抵消的目的是获得干扰信号的估计，并将其从损坏信号中减去，从而获得无噪声信号。比较了自适应滤波器输入非平稳时LMS和NLMS算法的跟踪性能。为此，滤波器采用自适应算法改变滤波器系数的值，使每次迭代后都能获得较好的逼近信号。最小均方算法(LMS)及其变体归一化LMS (NLMS)是目前应用最广泛的两种自适应算法。在语音信号干扰消除的情况下，本文对LMS和NLMS进行了比较分析。对于每种算法，分析了滤波器长度和步长两个参数对算法的影响。最后，比较了两种算法在不同情况下的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2011 International Conference on Computational Intelligence and Communication Networks

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