Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l0-norm algorithm, is proposed for multiple measu...
| Main Authors: | , , |
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| Format: | Journal Article |
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MDPI Publishing
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/71700 |
| _version_ | 1848762549232730112 |
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| author | Liu, J. Zhou, W. Juwono, Filbert Hilman |
| author_facet | Liu, J. Zhou, W. Juwono, Filbert Hilman |
| author_sort | Liu, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 by the authors. Licensee MDPI, Basel, Switzerland. Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l0-norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l0-norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l1-norm minimization based methods, such as l1-SVD (singular value decomposition), RV (real-valued) l1-SVD and RV l1-SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance. |
| first_indexed | 2025-11-14T10:49:20Z |
| format | Journal Article |
| id | curtin-20.500.11937-71700 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:49:20Z |
| publishDate | 2017 |
| publisher | MDPI Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-717002021-01-08T07:54:28Z Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar Liu, J. Zhou, W. Juwono, Filbert Hilman © 2017 by the authors. Licensee MDPI, Basel, Switzerland. Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l0-norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l0-norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l1-norm minimization based methods, such as l1-SVD (singular value decomposition), RV (real-valued) l1-SVD and RV l1-SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance. 2017 Journal Article http://hdl.handle.net/20.500.11937/71700 10.3390/s17051068 http://creativecommons.org/licenses/by/4.0/ MDPI Publishing fulltext |
| spellingShingle | Liu, J. Zhou, W. Juwono, Filbert Hilman Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar |
| title | Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar |
| title_full | Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar |
| title_fullStr | Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar |
| title_full_unstemmed | Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar |
| title_short | Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar |
| title_sort | joint smoothed l0-norm doa estimation algorithm for multiple measurement vectors in mimo radar |
| url | http://hdl.handle.net/20.500.11937/71700 |