On the mean-square performance of the constrained LMS algorithm

被引：41

作者：

Arablouei, Reza ^{[1
]}

Dogancay, Kutluyil ^{[2
]}

Werner, Stefan ^{[3
]}

机构：

[1] CSIRO, Pullenvale, Qld, Australia

[2] Univ S Australia, Sch Engn, Mawson Lakes, SA, Australia

[3] Aalto Univ, Sch Elect Engn, Dept Signal Proc & Acoust, Espoo, Finland

来源：

SIGNAL PROCESSING | 2015年 / 117卷

关键词：

Constrained least mean-square; Linearly-constrained adaptive Filtering; Mean-square deviation; Mean-square stability; Performance analysis;

D O I：

10.1016/j.sigpro.2015.05.011

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 so-called constrained least mean-square algorithm is one of the most commonly used linear-equality-constrained adaptive filtering algorithms. Its main advantages are adaptability and relative simplicity. In order to gain analytical insights into the performance of this algorithm, we examine its mean-square performance and derive theoretical expressions for its transient and steady-state mean-square deviation. Our methodology is inspired by the principle of energy conservation in adaptive filters. Simulation results corroborate the accuracy of the derived formula. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：192 / 197

页数：6

共 33 条

[1]

Abadir K. M., 2005, MATRIX ALGEBRA, V1

[2]

[Anonymous], 2013, Adaptive Filtering, DOI [10.1007/978-1-4614-4106-9, DOI 10.1007/978-1-4614-4106-9]

[3]

[Anonymous], 2006, ADAPTIVE CONTROL TUT, DOI DOI 10.1137/1.9780898718652

[4] The constrained conjugate gradient algorithm [J].