Robust Proportionate Adaptive Filter Architectures Under Impulsive Noise

被引：6

作者：

Mula, Subrahmanyam ^{[1
]}

Gogineni, Vinay Chakravarthi ^{[1
]}

Dhar, Anindya Sundar ^{[1
]}

机构：

[1] IIT Kharagpur, Dept Elect & Elect Commun Engn, Kharagpur 721302, W Bengal, India

来源：

IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS | 2019年 / 27卷 / 05期

关键词：

Adaptive filters; impulsive noise; proportionate-type algorithms; sparse system identification; VLSI architectures; MAXIMUM CORRENTROPY CRITERION;

D O I：

10.1109/TVLSI.2019.2892383

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This brief proposes robust adaptive filtering algorithms and their VLSI architectures for sparse system identification under impulsive noise. Several robust algorithms are derived by combining error nonlinear adaptive filtering algorithms with proportionate adaptation. We make a comparative study of the derived algorithms and their VLSI architectures in terms of convergence rate and hardware complexity to show that the hardware overhead is negligible for the achieved improvement in robustness.

引用

页码：1223 / 1227

页数：5

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