Recursive least mean square algorithm based on fourth-order statistics

Ran Tao; Wei Qiang Zhang; Yong Feng Ma

Recursive least mean square algorithm based on fourth-order statistics

Ran Tao^*
, Wei Qiang Zhang
, Yong Feng Ma

^*此作品的通讯作者

信息与电子学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

An LMS algorithm based on one dimensional slice of fourth-order statistics is proposed and its recursive form is given. This algorithm can suppress the correlated Gaussian noise effectively, and its performance is better than the conventional autocorrelation-based LMS algorithm. The recursive form reduces the computational complexity, and it can meet the need of real-time processing. Simulation results are presented to demonstrate the effectiveness of this approach. The algorithm can be used to estimate multi-path coefficients in radar, sonar and communication systems.

源语言	英语
页（从-至）	442-445
页数	4
期刊	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
卷	24
期	5
出版状态	已出版 - 5月 2004

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引用此

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title = "Recursive least mean square algorithm based on fourth-order statistics",

abstract = "An LMS algorithm based on one dimensional slice of fourth-order statistics is proposed and its recursive form is given. This algorithm can suppress the correlated Gaussian noise effectively, and its performance is better than the conventional autocorrelation-based LMS algorithm. The recursive form reduces the computational complexity, and it can meet the need of real-time processing. Simulation results are presented to demonstrate the effectiveness of this approach. The algorithm can be used to estimate multi-path coefficients in radar, sonar and communication systems.",

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AU - Zhang, Wei Qiang

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N2 - An LMS algorithm based on one dimensional slice of fourth-order statistics is proposed and its recursive form is given. This algorithm can suppress the correlated Gaussian noise effectively, and its performance is better than the conventional autocorrelation-based LMS algorithm. The recursive form reduces the computational complexity, and it can meet the need of real-time processing. Simulation results are presented to demonstrate the effectiveness of this approach. The algorithm can be used to estimate multi-path coefficients in radar, sonar and communication systems.

AB - An LMS algorithm based on one dimensional slice of fourth-order statistics is proposed and its recursive form is given. This algorithm can suppress the correlated Gaussian noise effectively, and its performance is better than the conventional autocorrelation-based LMS algorithm. The recursive form reduces the computational complexity, and it can meet the need of real-time processing. Simulation results are presented to demonstrate the effectiveness of this approach. The algorithm can be used to estimate multi-path coefficients in radar, sonar and communication systems.

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KW - LMS algorithm

KW - Recursive form

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Recursive least mean square algorithm based on fourth-order statistics

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