A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution
The second and the third sentences of the abstract are changed and the shorter abstract is given as follows. To recover the nonstationary signal in complicated noise environment without distortion, a novel general design of fractional filter is proposed and applied to eliminate the Wigner cross-term...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/189308 |
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| _version_ | 1850104207528427520 |
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| author | Jiexiao Yu Kaihua Liu Liang Zhang Peng Luo |
| author_facet | Jiexiao Yu Kaihua Liu Liang Zhang Peng Luo |
| author_sort | Jiexiao Yu |
| collection | DOAJ |
| description | The second and the third sentences of the abstract are changed and the shorter abstract is given as follows. To recover the nonstationary signal in complicated noise environment without distortion, a novel general design of fractional filter is proposed and applied to eliminate the Wigner cross-term. A time-frequency binary image is obtained from the time-frequency distribution of the observed signal and the optimal separating lines are determined by the support vector machine (SVM) classifier where the image boundary extraction algorithms are used to construct the training set of SVM. After that, the parameters and transfer function of filter can be determined by the parameters of the separating lines directly in the case of linear separability or line segments after the piecewise linear fitting of the separating curves in the case of nonlinear separability. Without any prior knowledge of signal and noise, this method can meet the reliability and universality simultaneously for filter design and realize the global optimization of filter parameters by machine learning even in the case of strong coupling between signal and noise. Furthermore, it could completely eliminate the cross-term in Wigner-Ville distribution (WVD) and the time-frequency distribution we get in the end has high resolution and good readability even when autoterms and cross-terms overlap. Simulation results verified the efficiency of this method. |
| format | Article |
| id | doaj-art-49fe256485204e7e863eb7ca7720b4ec |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-49fe256485204e7e863eb7ca7720b4ec2025-08-20T02:39:22ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/189308189308A Novel Fractional Filter Design and Cross-Term Elimination in Wigner DistributionJiexiao Yu0Kaihua Liu1Liang Zhang2Peng Luo3 School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China Hebei Electric Power Institute, Shijiazhuang 050021, ChinaThe second and the third sentences of the abstract are changed and the shorter abstract is given as follows. To recover the nonstationary signal in complicated noise environment without distortion, a novel general design of fractional filter is proposed and applied to eliminate the Wigner cross-term. A time-frequency binary image is obtained from the time-frequency distribution of the observed signal and the optimal separating lines are determined by the support vector machine (SVM) classifier where the image boundary extraction algorithms are used to construct the training set of SVM. After that, the parameters and transfer function of filter can be determined by the parameters of the separating lines directly in the case of linear separability or line segments after the piecewise linear fitting of the separating curves in the case of nonlinear separability. Without any prior knowledge of signal and noise, this method can meet the reliability and universality simultaneously for filter design and realize the global optimization of filter parameters by machine learning even in the case of strong coupling between signal and noise. Furthermore, it could completely eliminate the cross-term in Wigner-Ville distribution (WVD) and the time-frequency distribution we get in the end has high resolution and good readability even when autoterms and cross-terms overlap. Simulation results verified the efficiency of this method.https://doi.org/10.1155/2015/189308 |
| spellingShingle | Jiexiao Yu Kaihua Liu Liang Zhang Peng Luo A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution International Journal of Distributed Sensor Networks |
| title | A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution |
| title_full | A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution |
| title_fullStr | A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution |
| title_full_unstemmed | A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution |
| title_short | A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution |
| title_sort | novel fractional filter design and cross term elimination in wigner distribution |
| url | https://doi.org/10.1155/2015/189308 |
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