Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition
Active noise control (ANC) algorithms have been developed within the adaptive algorithm framework. However, multichannel ANC systems, which include numerous reference sensors, control speakers, and error microphones, require a very long control filter converging time for control filter estimation. T...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/sil2/2128989 |
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| author | Hakjun Lee Youngjin Park |
| author_facet | Hakjun Lee Youngjin Park |
| author_sort | Hakjun Lee |
| collection | DOAJ |
| description | Active noise control (ANC) algorithms have been developed within the adaptive algorithm framework. However, multichannel ANC systems, which include numerous reference sensors, control speakers, and error microphones, require a very long control filter converging time for control filter estimation. Traditional system identification methods, such as the Wiener filter method, are better suited for such systems because of their relatively shorter converging time. However, they require large amounts of data to achieve accurate statistical estimation. Therefore, this article proposes a control filter estimation method that requires only a short length of data. An iterative Wiener filter solution using Kronecker product decomposition for multichannel ANC systems converts the filter estimation process by breaking down the extensive control filter into multiple shorter control filters through Kronecker product decomposition. This decomposition effectively reduces the high-dimensional system identification problem into manageable low-dimensional ones. Numerical simulations demonstrate the superiority of the proposed method over conventional Wiener filter techniques, especially in scenarios when limited data are available for control filter estimation. |
| format | Article |
| id | doaj-art-b2d39cc0ac6c4ff8a2d1207a5ef0b6c8 |
| institution | DOAJ |
| issn | 1751-9683 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-b2d39cc0ac6c4ff8a2d1207a5ef0b6c82025-08-20T03:12:39ZengWileyIET Signal Processing1751-96832025-01-01202510.1049/sil2/2128989Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product DecompositionHakjun Lee0Youngjin Park1Department of Mechanical EngineeringDepartment of Mechanical EngineeringActive noise control (ANC) algorithms have been developed within the adaptive algorithm framework. However, multichannel ANC systems, which include numerous reference sensors, control speakers, and error microphones, require a very long control filter converging time for control filter estimation. Traditional system identification methods, such as the Wiener filter method, are better suited for such systems because of their relatively shorter converging time. However, they require large amounts of data to achieve accurate statistical estimation. Therefore, this article proposes a control filter estimation method that requires only a short length of data. An iterative Wiener filter solution using Kronecker product decomposition for multichannel ANC systems converts the filter estimation process by breaking down the extensive control filter into multiple shorter control filters through Kronecker product decomposition. This decomposition effectively reduces the high-dimensional system identification problem into manageable low-dimensional ones. Numerical simulations demonstrate the superiority of the proposed method over conventional Wiener filter techniques, especially in scenarios when limited data are available for control filter estimation.http://dx.doi.org/10.1049/sil2/2128989 |
| spellingShingle | Hakjun Lee Youngjin Park Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition IET Signal Processing |
| title | Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition |
| title_full | Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition |
| title_fullStr | Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition |
| title_full_unstemmed | Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition |
| title_short | Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition |
| title_sort | control filter estimation for multichannel active noise control using kronecker product decomposition |
| url | http://dx.doi.org/10.1049/sil2/2128989 |
| work_keys_str_mv | AT hakjunlee controlfilterestimationformultichannelactivenoisecontrolusingkroneckerproductdecomposition AT youngjinpark controlfilterestimationformultichannelactivenoisecontrolusingkroneckerproductdecomposition |