Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System
The estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency plane is obt...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/2/456 |
| _version_ | 1797443379722715136 |
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| author | Yangyang Li Dzati Athiar Ramli |
| author_facet | Yangyang Li Dzati Athiar Ramli |
| author_sort | Yangyang Li |
| collection | DOAJ |
| description | The estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency plane is obtained by rotating the time–frequency plane after the short-time Fourier transform. This plane represents the fine characteristics of the mixed signal in the time domain and the frequency domain. The rotation angle is determined by global searching for the minimum L1 norm to make the mixed signal sufficiently sparse. The obtained time–frequency points do not need single source point detection, reducing the calculation amount of the original algorithm, and the insensitivity to noise in the fractional domain improves the robustness of the algorithm in the noise environment. The simulation results show that the sparsity of the mixed signal and the estimation accuracy of the mixed matrix are improved. Compared with the existing mixed matrix estimation algorithms, the proposed method is effective. |
| first_indexed | 2024-03-09T12:56:11Z |
| format | Article |
| id | doaj.art-b3b7122d073d4249bce63587e3b12be7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T12:56:11Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b3b7122d073d4249bce63587e3b12be72023-11-30T22:00:43ZengMDPI AGElectronics2079-92922023-01-0112245610.3390/electronics12020456Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation SystemYangyang Li0Dzati Athiar Ramli1School of Electrical and Electronic Engineering, USM Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, MalaysiaSchool of Electrical and Electronic Engineering, USM Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, MalaysiaThe estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency plane is obtained by rotating the time–frequency plane after the short-time Fourier transform. This plane represents the fine characteristics of the mixed signal in the time domain and the frequency domain. The rotation angle is determined by global searching for the minimum L1 norm to make the mixed signal sufficiently sparse. The obtained time–frequency points do not need single source point detection, reducing the calculation amount of the original algorithm, and the insensitivity to noise in the fractional domain improves the robustness of the algorithm in the noise environment. The simulation results show that the sparsity of the mixed signal and the estimation accuracy of the mixed matrix are improved. Compared with the existing mixed matrix estimation algorithms, the proposed method is effective.https://www.mdpi.com/2079-9292/12/2/456underdetermined blind source separation (UBSS)mixed matrix estimationFractional Fourier Transform (FrFT)noise suppressionmini-L1 norm of optimal transformation order |
| spellingShingle | Yangyang Li Dzati Athiar Ramli Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System Electronics underdetermined blind source separation (UBSS) mixed matrix estimation Fractional Fourier Transform (FrFT) noise suppression mini-L1 norm of optimal transformation order |
| title | Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System |
| title_full | Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System |
| title_fullStr | Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System |
| title_full_unstemmed | Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System |
| title_short | Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System |
| title_sort | research on mixed matrix estimation algorithm based on improved sparse representation model in underdetermined blind source separation system |
| topic | underdetermined blind source separation (UBSS) mixed matrix estimation Fractional Fourier Transform (FrFT) noise suppression mini-L1 norm of optimal transformation order |
| url | https://www.mdpi.com/2079-9292/12/2/456 |
| work_keys_str_mv | AT yangyangli researchonmixedmatrixestimationalgorithmbasedonimprovedsparserepresentationmodelinunderdeterminedblindsourceseparationsystem AT dzatiathiarramli researchonmixedmatrixestimationalgorithmbasedonimprovedsparserepresentationmodelinunderdeterminedblindsourceseparationsystem |