Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar
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 l 0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/5/1068 |
| _version_ | 1798005493602451456 |
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| author | Jing Liu Weidong Zhou Filbert H. Juwono |
| author_facet | Jing Liu Weidong Zhou Filbert H. Juwono |
| author_sort | Jing Liu |
| collection | DOAJ |
| description | 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 l 0 -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 l 0 -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 l 1 -norm minimization based methods, such as l 1 -SVD (singular value decomposition), RV (real-valued) l 1 -SVD and RV l 1 -SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance. |
| first_indexed | 2024-04-11T12:41:16Z |
| format | Article |
| id | doaj.art-b217665b7fb64c1fbd87444b3d872528 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:41:16Z |
| publishDate | 2017-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b217665b7fb64c1fbd87444b3d8725282022-12-22T04:23:30ZengMDPI AGSensors1424-82202017-05-01175106810.3390/s17051068s17051068Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO RadarJing Liu0Weidong Zhou1Filbert H. Juwono2College of Automation, Harbin Engineering University, Harbin 150001, ChinaCollege of Automation, Harbin Engineering University, Harbin 150001, ChinaDepartment of Electrical Engineering, Universitas Indonesia, Depok 16424, IndonesiaDirection-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 l 0 -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 l 0 -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 l 1 -norm minimization based methods, such as l 1 -SVD (singular value decomposition), RV (real-valued) l 1 -SVD and RV l 1 -SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance.http://www.mdpi.com/1424-8220/17/5/1068direction-of-arrival estimationjoint smoothed l0-normmultiple measurement vectorssparse signal reconstructionMIMO radar |
| spellingShingle | Jing Liu Weidong Zhou Filbert H. Juwono Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar Sensors direction-of-arrival estimation joint smoothed l0-norm multiple measurement vectors sparse signal reconstruction 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 |
| topic | direction-of-arrival estimation joint smoothed l0-norm multiple measurement vectors sparse signal reconstruction MIMO radar |
| url | http://www.mdpi.com/1424-8220/17/5/1068 |
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