A robust algorithm for adaptive FIR filtering and its performance analysis with additive contaminated-Gaussian noise

S. Bang, S. Ann
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引用次数: 11

Abstract

Abstruct- We introduce a steepest descent linear adaptive algorithm, the proportion-sign algorithm (PSA), lo make the least mean square (LMS) algorithm robust to impulsive interference occurring in the desired response. Its performance analysis is presented when the signals are from zero-mean jlointly stationary Gaussian processes and the additive noise to the (desired response is from a zero-mean stationary contaminated-Gaussian (CG) process which is usually used to represent impulsive interference. Since a special case of the PSA becomes the LMS algorithm, the analysis of the LMS is also obtained as a by-product. By adding a minimal amount of computational complexity, thie PSA improves to some degree the convergence speed over the LMS algorithm without overly degrading the steady-state error performance for Gaussian noise. In addition, since the first derivative of its cost function with respect to estimation error is bounded, it has the properties of robustness to impulsive interference occurring in the desired response while the LMS algorithm is vulnerable to it. Computer simulations are used to demonstrate the validity of our analysis and the robustness of the PSA compared with the LMS algorithm.
一种鲁棒自适应FIR滤波算法及其加性高斯污染噪声的性能分析
摘要:本文引入了一种最陡下降线性自适应算法——比例符号算法(PSA),使最小均方(LMS)算法对期望响应中出现的脉冲干扰具有鲁棒性。给出了当信号来自于零均值均匀平稳高斯过程,期望响应的加性噪声来自于通常用于表示脉冲干扰的零均值平稳污染高斯过程时的性能分析。由于PSA的一个特例成为LMS算法,因此LMS的分析也作为副产品得到。通过增加最小的计算复杂度,PSA在一定程度上提高了LMS算法的收敛速度,而不会过度降低高斯噪声的稳态误差性能。此外,由于其代价函数对估计误差的一阶导数是有界的,因此它对期望响应中出现的脉冲干扰具有鲁棒性,而LMS算法易受其影响。计算机仿真证明了我们的分析的有效性以及与LMS算法相比,PSA算法的鲁棒性。
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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