Application of local polynomial estimation in suppressing strong chaotic noise

In this paper, we propose a new method that combines chaotic series phase space reconstruction and local polynomial estimation to solve the problem of suppressing strong chaotic noise. First, chaotic noise time series are reconstructed to obtain multivariate time series according to Takens delay emb...

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Bibliographic Details
Published inChinese physics B Vol. 21; no. 2; pp. 181 - 186
Main Author 苏理云 马艳菊 李姣军
Format Journal Article
LanguageEnglish
Published 01.02.2012
Subjects
Chaos theory
Delay
Mathematical analysis
Mean square values
Noise
Polynomials
Retarding
Simulation
Time series
估计方法
信号干扰比
多元时间序列
局部多项式
应用
有用信号
混沌噪声
相空间重构
Online AccessGet full text
ISSN1674-1056
2058-3834
1741-4199
DOI10.1088/1674-1056/21/2/020508

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Summary:In this paper, we propose a new method that combines chaotic series phase space reconstruction and local polynomial estimation to solve the problem of suppressing strong chaotic noise. First, chaotic noise time series are reconstructed to obtain multivariate time series according to Takens delay embedding theorem. Then the chaotic noise is estimated accurately using local polynomial estimation method. After chaotic noise is separated from observation signal, we can get the estimation of the useful signal. This local polynomial estimation method can combine the advantages of local and global law. Finally, it makes the estimation more exactly and we can calculate the formula of mean square error theoretically. The simulation results show that the method is effective for the suppression of strong chaotic noise when the signal to interference ratio is low.
Bibliography:In this paper, we propose a new method that combines chaotic series phase space reconstruction and local polynomial estimation to solve the problem of suppressing strong chaotic noise. First, chaotic noise time series are reconstructed to obtain multivariate time series according to Takens delay embedding theorem. Then the chaotic noise is estimated accurately using local polynomial estimation method. After chaotic noise is separated from observation signal, we can get the estimation of the useful signal. This local polynomial estimation method can combine the advantages of local and global law. Finally, it makes the estimation more exactly and we can calculate the formula of mean square error theoretically. The simulation results show that the method is effective for the suppression of strong chaotic noise when the signal to interference ratio is low.
Su Li-Yun, Ma Yan-Ju, Li Jiao-Jun a） School of Mathematics and Statistics, Chongqing University of Technology, Chongqing 400054, China b） School of Electronic Information and Automation, Chongqing University of Technology, Chongqing 400054, China
strong chaotic noise, local polynomial estimation, weak signal detection
11-5639/O4
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1674-1056
2058-3834
1741-4199
DOI:10.1088/1674-1056/21/2/020508