Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals
In a wireless sensor network, the signal received by the terminal processor is usually a complex single channel hybrid chaotic signal. The engineering needs to separate the useful signal from the mixed signal to perform the next transmission analysis. Since chaotic signals are nonlinear and unpredic...
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2018-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/9571510 |
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doaj-90b87143ed8a461da0ffcf8b159d70722020-11-24T22:23:02ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/95715109571510Analysis of Single Channel Blind Source Separation Algorithm for Chaotic SignalsJiai He0Yuxiao Song1Panpan Du2Lei Xu3School of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou, ChinaIn a wireless sensor network, the signal received by the terminal processor is usually a complex single channel hybrid chaotic signal. The engineering needs to separate the useful signal from the mixed signal to perform the next transmission analysis. Since chaotic signals are nonlinear and unpredictable, traditional blind separation algorithms cannot effectively separate chaotic signals. Aiming to correct these problems—based on the particle filter estimation algorithm—an extended Kalman particle filter algorithm (EPF) and an unscented Kalman particle filter algorithm (UPF) are proposed to solve the single channel blind separation problem of chaotic signals. Mixing chaotic signals of different intensities performs blind source separation. Using different evaluation indexes carries out the experiment and performance can be analyzed. The results show that the proposed algorithm effectively separates the mixed chaotic signals.http://dx.doi.org/10.1155/2018/9571510 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiai He Yuxiao Song Panpan Du Lei Xu |
spellingShingle |
Jiai He Yuxiao Song Panpan Du Lei Xu Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals Mathematical Problems in Engineering |
author_facet |
Jiai He Yuxiao Song Panpan Du Lei Xu |
author_sort |
Jiai He |
title |
Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals |
title_short |
Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals |
title_full |
Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals |
title_fullStr |
Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals |
title_full_unstemmed |
Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals |
title_sort |
analysis of single channel blind source separation algorithm for chaotic signals |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
In a wireless sensor network, the signal received by the terminal processor is usually a complex single channel hybrid chaotic signal. The engineering needs to separate the useful signal from the mixed signal to perform the next transmission analysis. Since chaotic signals are nonlinear and unpredictable, traditional blind separation algorithms cannot effectively separate chaotic signals. Aiming to correct these problems—based on the particle filter estimation algorithm—an extended Kalman particle filter algorithm (EPF) and an unscented Kalman particle filter algorithm (UPF) are proposed to solve the single channel blind separation problem of chaotic signals. Mixing chaotic signals of different intensities performs blind source separation. Using different evaluation indexes carries out the experiment and performance can be analyzed. The results show that the proposed algorithm effectively separates the mixed chaotic signals. |
url |
http://dx.doi.org/10.1155/2018/9571510 |
work_keys_str_mv |
AT jiaihe analysisofsinglechannelblindsourceseparationalgorithmforchaoticsignals AT yuxiaosong analysisofsinglechannelblindsourceseparationalgorithmforchaoticsignals AT panpandu analysisofsinglechannelblindsourceseparationalgorithmforchaoticsignals AT leixu analysisofsinglechannelblindsourceseparationalgorithmforchaoticsignals |
_version_ |
1725766239760941056 |