Reweighted smoothed l0-norm based DOA estimation for MIMO radar
© 2017 Elsevier B.V. In this paper, a reweighted smoothed l0-norm algorithm is proposed for direction-of-arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar. The proposed method firstly performs the vectorization operation on the covariance matrix, which is calculated...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier BV
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/72025 |
| _version_ | 1848762639348400128 |
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| author | Liu, J. Zhou, W. Juwono, Filbert Hilman Huang, D. |
| author_facet | Liu, J. Zhou, W. Juwono, Filbert Hilman Huang, D. |
| author_sort | Liu, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 Elsevier B.V. In this paper, a reweighted smoothed l0-norm algorithm is proposed for direction-of-arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar. The proposed method firstly performs the vectorization operation on the covariance matrix, which is calculated from the latest received data matrix obtained by a reduced dimensional transformation. Then a weighted matrix is introduced to transform the covariance estimation errors into a Gaussian white vector, and the proposed method further constructs the other reweighted vector to enhance sparse solution. Finally, a reweighted smoothed l0-norm minimization framework with a reweighted continuous function is designed, based on which the sparse solution is obtained by using a decreasing parameter sequence and the steepest ascent algorithm. Consequently, DOA estimation is accomplished by searching the spectrum of the solution. Compared with the conventional l1-norm minimization based methods, the proposed reweighted smoothed l0-norm algorithm significantly reduces the computation time of DOA estimation. The proposed method is about two orders of magnitude faster than the l1-SVD, reweighted l1-SVD and RV l1-SRACV algorithms. Meanwhile, it provides higher spatial angular resolution and better angle estimation performance. Simulation results are used to verify the effectiveness and advantages of the proposed method. |
| first_indexed | 2025-11-14T10:50:46Z |
| format | Journal Article |
| id | curtin-20.500.11937-72025 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:50:46Z |
| publishDate | 2017 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-720252023-08-02T06:39:12Z Reweighted smoothed l0-norm based DOA estimation for MIMO radar Liu, J. Zhou, W. Juwono, Filbert Hilman Huang, D. © 2017 Elsevier B.V. In this paper, a reweighted smoothed l0-norm algorithm is proposed for direction-of-arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar. The proposed method firstly performs the vectorization operation on the covariance matrix, which is calculated from the latest received data matrix obtained by a reduced dimensional transformation. Then a weighted matrix is introduced to transform the covariance estimation errors into a Gaussian white vector, and the proposed method further constructs the other reweighted vector to enhance sparse solution. Finally, a reweighted smoothed l0-norm minimization framework with a reweighted continuous function is designed, based on which the sparse solution is obtained by using a decreasing parameter sequence and the steepest ascent algorithm. Consequently, DOA estimation is accomplished by searching the spectrum of the solution. Compared with the conventional l1-norm minimization based methods, the proposed reweighted smoothed l0-norm algorithm significantly reduces the computation time of DOA estimation. The proposed method is about two orders of magnitude faster than the l1-SVD, reweighted l1-SVD and RV l1-SRACV algorithms. Meanwhile, it provides higher spatial angular resolution and better angle estimation performance. Simulation results are used to verify the effectiveness and advantages of the proposed method. 2017 Journal Article http://hdl.handle.net/20.500.11937/72025 10.1016/j.sigpro.2017.01.034 Elsevier BV restricted |
| spellingShingle | Liu, J. Zhou, W. Juwono, Filbert Hilman Huang, D. Reweighted smoothed l0-norm based DOA estimation for MIMO radar |
| title | Reweighted smoothed l0-norm based DOA estimation for MIMO radar |
| title_full | Reweighted smoothed l0-norm based DOA estimation for MIMO radar |
| title_fullStr | Reweighted smoothed l0-norm based DOA estimation for MIMO radar |
| title_full_unstemmed | Reweighted smoothed l0-norm based DOA estimation for MIMO radar |
| title_short | Reweighted smoothed l0-norm based DOA estimation for MIMO radar |
| title_sort | reweighted smoothed l0-norm based doa estimation for mimo radar |
| url | http://hdl.handle.net/20.500.11937/72025 |