Enhanced NMF Separation of Mixed Signals in Strong Noise Environment
Separation of mixed signals from a noisy environment without prior conditions is one of the difficulties in blind signal separation. To solve the problem of poor separation effect of mixed signals in a strong noise environment, we propose an enhanced non-negative matrix factorization method in this...
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doaj-a6fd2a6aa31341f0a759792c8c6ba2572021-03-29T23:23:09ZengIEEEIEEE Access2169-35362019-01-017846498465710.1109/ACCESS.2019.29219928734049Enhanced NMF Separation of Mixed Signals in Strong Noise EnvironmentLiuyang Gao0Peng Dong1Nae Zheng2Yinghua Tian3National Digital Switching System Engineering and Technological Research and Development Center (NDSC), Information Engineering University (IEU), Zhengzhou, ChinaNational Digital Switching System Engineering and Technological Research and Development Center (NDSC), Information Engineering University (IEU), Zhengzhou, ChinaNational Digital Switching System Engineering and Technological Research and Development Center (NDSC), Information Engineering University (IEU), Zhengzhou, ChinaNational Digital Switching System Engineering and Technological Research and Development Center (NDSC), Information Engineering University (IEU), Zhengzhou, ChinaSeparation of mixed signals from a noisy environment without prior conditions is one of the difficulties in blind signal separation. To solve the problem of poor separation effect of mixed signals in a strong noise environment, we propose an enhanced non-negative matrix factorization method in this paper. By extending the Kullback-Leibler divergence form, this method adopts a new target signal and noise estimation algorithm to overcome the shortcomings of existing methods in noise estimation. Furthermore, combining with the least squares algorithm, the computational complexity is effectively reduced, and the computational efficiency of the algorithm is improved while the source signals are well estimated. The theoretical analysis and simulation results show that the proposed algorithm is better than the existing algorithms in terms of the source signal separation from mixed signals with noise, especially when the signal and noise energy are equivalent and the mixed signals are completely obliterated in the noise, the proposed algorithm has more obvious advantages than the existing algorithms, while the operation efficiency has been improved.https://ieeexplore.ieee.org/document/8734049/Signal separationnon-negative matrix factorizationKullback-Leibler divergenceleast squares |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liuyang Gao Peng Dong Nae Zheng Yinghua Tian |
spellingShingle |
Liuyang Gao Peng Dong Nae Zheng Yinghua Tian Enhanced NMF Separation of Mixed Signals in Strong Noise Environment IEEE Access Signal separation non-negative matrix factorization Kullback-Leibler divergence least squares |
author_facet |
Liuyang Gao Peng Dong Nae Zheng Yinghua Tian |
author_sort |
Liuyang Gao |
title |
Enhanced NMF Separation of Mixed Signals in Strong Noise Environment |
title_short |
Enhanced NMF Separation of Mixed Signals in Strong Noise Environment |
title_full |
Enhanced NMF Separation of Mixed Signals in Strong Noise Environment |
title_fullStr |
Enhanced NMF Separation of Mixed Signals in Strong Noise Environment |
title_full_unstemmed |
Enhanced NMF Separation of Mixed Signals in Strong Noise Environment |
title_sort |
enhanced nmf separation of mixed signals in strong noise environment |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Separation of mixed signals from a noisy environment without prior conditions is one of the difficulties in blind signal separation. To solve the problem of poor separation effect of mixed signals in a strong noise environment, we propose an enhanced non-negative matrix factorization method in this paper. By extending the Kullback-Leibler divergence form, this method adopts a new target signal and noise estimation algorithm to overcome the shortcomings of existing methods in noise estimation. Furthermore, combining with the least squares algorithm, the computational complexity is effectively reduced, and the computational efficiency of the algorithm is improved while the source signals are well estimated. The theoretical analysis and simulation results show that the proposed algorithm is better than the existing algorithms in terms of the source signal separation from mixed signals with noise, especially when the signal and noise energy are equivalent and the mixed signals are completely obliterated in the noise, the proposed algorithm has more obvious advantages than the existing algorithms, while the operation efficiency has been improved. |
topic |
Signal separation non-negative matrix factorization Kullback-Leibler divergence least squares |
url |
https://ieeexplore.ieee.org/document/8734049/ |
work_keys_str_mv |
AT liuyanggao enhancednmfseparationofmixedsignalsinstrongnoiseenvironment AT pengdong enhancednmfseparationofmixedsignalsinstrongnoiseenvironment AT naezheng enhancednmfseparationofmixedsignalsinstrongnoiseenvironment AT yinghuatian enhancednmfseparationofmixedsignalsinstrongnoiseenvironment |
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1724189675994742784 |