A unified framework for least square and mean square based adaptive filtering algorithms

Zhongkai Zhang; Tamal Bose; Jacob Gunther

doi:10.1109/ISCAS.2005.1465588

A unified framework for least square and mean square based adaptive filtering algorithms

Zhongkai Zhang, Tamal Bose, Jacob Gunther

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

This paper presents a unified framework for adaptive filters based on a line search method. Expressions for this unified framework are derived. Based on this framework new algorithms are derived, namely, Diagonal Q-correlation matrix Least Square algorithm (DQLS), Block Diagonal Q-correlation matrix Least Square algorithm (BDQLS) and their reduced complexity variants. It is shown that both DQLS and BDQLS have less computational complexity compared to EDS and RLS, and better performance than LMS.

Original language	English (US)
Article number	1465588
Pages (from-to)	4325-4328
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
DOIs	https://doi.org/10.1109/ISCAS.2005.1465588
State	Published - 2005
Externally published	Yes
Event	IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan Duration: May 23 2005 → May 26 2005

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/ISCAS.2005.1465588

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title = "A unified framework for least square and mean square based adaptive filtering algorithms",

abstract = "This paper presents a unified framework for adaptive filters based on a line search method. Expressions for this unified framework are derived. Based on this framework new algorithms are derived, namely, Diagonal Q-correlation matrix Least Square algorithm (DQLS), Block Diagonal Q-correlation matrix Least Square algorithm (BDQLS) and their reduced complexity variants. It is shown that both DQLS and BDQLS have less computational complexity compared to EDS and RLS, and better performance than LMS.",

author = "Zhongkai Zhang and Tamal Bose and Jacob Gunther",

year = "2005",

doi = "10.1109/ISCAS.2005.1465588",

language = "English (US)",

pages = "4325--4328",

journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 ; Conference date: 23-05-2005 Through 26-05-2005",

}

TY - JOUR

T1 - A unified framework for least square and mean square based adaptive filtering algorithms

AU - Zhang, Zhongkai

AU - Bose, Tamal

AU - Gunther, Jacob

PY - 2005

Y1 - 2005

N2 - This paper presents a unified framework for adaptive filters based on a line search method. Expressions for this unified framework are derived. Based on this framework new algorithms are derived, namely, Diagonal Q-correlation matrix Least Square algorithm (DQLS), Block Diagonal Q-correlation matrix Least Square algorithm (BDQLS) and their reduced complexity variants. It is shown that both DQLS and BDQLS have less computational complexity compared to EDS and RLS, and better performance than LMS.

AB - This paper presents a unified framework for adaptive filters based on a line search method. Expressions for this unified framework are derived. Based on this framework new algorithms are derived, namely, Diagonal Q-correlation matrix Least Square algorithm (DQLS), Block Diagonal Q-correlation matrix Least Square algorithm (BDQLS) and their reduced complexity variants. It is shown that both DQLS and BDQLS have less computational complexity compared to EDS and RLS, and better performance than LMS.

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U2 - 10.1109/ISCAS.2005.1465588

DO - 10.1109/ISCAS.2005.1465588

M3 - Conference article

AN - SCOPUS:67649094490

SN - 0271-4310

SP - 4325

EP - 4328

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

M1 - 1465588

T2 - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005

Y2 - 23 May 2005 through 26 May 2005

ER -

A unified framework for least square and mean square based adaptive filtering algorithms

Abstract

ASJC Scopus subject areas

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