An effective source number enumeration approach based on SEMD
In signal processing, empirical mode decomposition (EMD) first decomposes the received single-channel signal into several intrinsic mode functions (IMFs) and a residual, and then uses machine learning methods for source number enumeration. EMD, however, has an end effect that can undermine the accur...
| Main Authors: | , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2022
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| _version_ | 1825938332556722176 |
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| author | Ge, Shengguo Mohd Rum, Siti Nurulain Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran |
| author_facet | Ge, Shengguo Mohd Rum, Siti Nurulain Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran |
| author_sort | Ge, Shengguo |
| collection | UPM |
| description | In signal processing, empirical mode decomposition (EMD) first decomposes the received single-channel signal into several intrinsic mode functions (IMFs) and a residual, and then uses machine learning methods for source number enumeration. EMD, however, has an end effect that can undermine the accuracy of source number enumeration. To address this issue, this paper proposed a new EMD method named Supplementary Empirical Mode Decomposition (SEMD), which improved the accuracy by extending the signal length. The proposed method can be better applied to the modal parameter identification of non-stationary and nonlinear data in the engineering field. This method first identifies two candidate extreme points, which are the closest to the function value of the first extreme point near the endpoint. Then, on one side of the candidate point, it finds a waveform similar to that at the endpoint. Finally, the maximum and minimum points at each end of the signal will be added to extend the length of the signal. The added extreme points are candidate extreme points in similar waveforms. For the improved source number enumeration method based on SEMD, the instantaneous phase is obtained first by SEMD and Hilbert transform (HT). Then, the instantaneous phase feature is extracted to obtain a high-dimensional eigenvalue vector. Finally, the back propagation (BP) neural network is used to predict the number of sources. Experiment shows that SEMD can effectively restrain the end effect, and the source number enumeration algorithm based on SEMD has a higher correct detection probability than others. |
| first_indexed | 2024-09-25T03:36:23Z |
| format | Article |
| id | upm.eprints-100233 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-09-25T03:36:23Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | upm.eprints-1002332024-07-11T04:11:17Z http://psasir.upm.edu.my/id/eprint/100233/ An effective source number enumeration approach based on SEMD Ge, Shengguo Mohd Rum, Siti Nurulain Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran In signal processing, empirical mode decomposition (EMD) first decomposes the received single-channel signal into several intrinsic mode functions (IMFs) and a residual, and then uses machine learning methods for source number enumeration. EMD, however, has an end effect that can undermine the accuracy of source number enumeration. To address this issue, this paper proposed a new EMD method named Supplementary Empirical Mode Decomposition (SEMD), which improved the accuracy by extending the signal length. The proposed method can be better applied to the modal parameter identification of non-stationary and nonlinear data in the engineering field. This method first identifies two candidate extreme points, which are the closest to the function value of the first extreme point near the endpoint. Then, on one side of the candidate point, it finds a waveform similar to that at the endpoint. Finally, the maximum and minimum points at each end of the signal will be added to extend the length of the signal. The added extreme points are candidate extreme points in similar waveforms. For the improved source number enumeration method based on SEMD, the instantaneous phase is obtained first by SEMD and Hilbert transform (HT). Then, the instantaneous phase feature is extracted to obtain a high-dimensional eigenvalue vector. Finally, the back propagation (BP) neural network is used to predict the number of sources. Experiment shows that SEMD can effectively restrain the end effect, and the source number enumeration algorithm based on SEMD has a higher correct detection probability than others. Institute of Electrical and Electronics Engineers 2022-09 Article PeerReviewed Ge, Shengguo and Mohd Rum, Siti Nurulain and Ibrahim, Hamidah and Marsilah, Erzam and Perumal, Thinagaran (2022) An effective source number enumeration approach based on SEMD. IEEE Access, 10. pp. 96066-96078. ISSN 2169-3536 https://ieeexplore.ieee.org/document/9881492 10.1109/ACCESS.2022.3204998 |
| spellingShingle | Ge, Shengguo Mohd Rum, Siti Nurulain Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran An effective source number enumeration approach based on SEMD |
| title | An effective source number enumeration approach based on SEMD |
| title_full | An effective source number enumeration approach based on SEMD |
| title_fullStr | An effective source number enumeration approach based on SEMD |
| title_full_unstemmed | An effective source number enumeration approach based on SEMD |
| title_short | An effective source number enumeration approach based on SEMD |
| title_sort | effective source number enumeration approach based on semd |
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