Robust Adaptation in Impulsive Noise

被引：41

作者：

Al-Sayed, Sara ^{[1
]}

Zoubir, Abdelhak M. ^{[1
]}

Sayed, Ali H. ^{[2
]}

机构：

[1] Tech Univ Darmstadt, Inst Nachrichtentech, Signal Proc Grp, D-64283 Darmstadt, Germany

[2] Univ Calif Los Angeles, Dept Elect Engn, Los Angeles, CA 90095 USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2016年 / 64卷 / 11期

基金：

美国国家科学基金会;

关键词：

Robust statistics; impulsive noise; error nonlinearity; mean-square-error performance; energy conservation; STEP-SIZE NLMS; ALGORITHMS; NONLINEARITIES;

D O I：

10.1109/TSP.2016.2535239

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The popular least-mean-squares (LMS) algorithm for adaptive filtering is nonrobust against impulsive noise in the measurements. The presence of this type of noise degrades the transient and steady-state performance of the algorithm. Since the distribution of the impulsive noise is generally unknown, a robust semi-parametric approach to adaptive filtering is warranted, where the output error nonlinearity is adapted jointly with the parameter of interest. In this paper, a robust adaptive filtering algorithm is developed that effectively learns and tracks the output error distribution to improve estimation performance. The performance of the algorithm is analyzed mathematically and validated experimentally.

引用

页码：2851 / 2865

页数：15

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