A nonconvex sparse recovery method for DOA estimation based on the trimmed lasso
Sparse direction-of-arrival (DOA) estimation methods can be formulated as a group-sparse optimization problem. Meanwhile, sparse recovery methods based on nonconvex penalty terms have been a hot topic in recent years due to their several appealing properties. Herein, this paper studies a new nonconv...
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| Published in: | Digital signal processing Vol. 153; p. 104628 |
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| Language: | English |
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01.10.2024
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| ISSN: | 1051-2004, 1095-4333 |
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| Abstract | Sparse direction-of-arrival (DOA) estimation methods can be formulated as a group-sparse optimization problem. Meanwhile, sparse recovery methods based on nonconvex penalty terms have been a hot topic in recent years due to their several appealing properties. Herein, this paper studies a new nonconvex regularized approach called the trimmed lasso for DOA estimation. We define the penalty term of the trimmed lasso in the multiple measurement vector model by ℓ2,1-norm. First, we use the smooth approximation function to change the nonconvex objective function to the convex weighted problem. Next, we derive sparse recovery guarantees based on the extended Restricted Isometry Property and regularization parameter for the trimmed lasso in the multiple measurement vector problem. Our proposed method can control the desired level of sparsity of estimators exactly and give a more precise solution to the DOA estimation problem. Numerical simulations show that our proposed method overperforms traditional approaches, which is more close to the Cramer-Rao bound. |
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| AbstractList | Sparse direction-of-arrival (DOA) estimation methods can be formulated as a group-sparse optimization problem. Meanwhile, sparse recovery methods based on nonconvex penalty terms have been a hot topic in recent years due to their several appealing properties. Herein, this paper studies a new nonconvex regularized approach called the trimmed lasso for DOA estimation. We define the penalty term of the trimmed lasso in the multiple measurement vector model by ℓ2,1-norm. First, we use the smooth approximation function to change the nonconvex objective function to the convex weighted problem. Next, we derive sparse recovery guarantees based on the extended Restricted Isometry Property and regularization parameter for the trimmed lasso in the multiple measurement vector problem. Our proposed method can control the desired level of sparsity of estimators exactly and give a more precise solution to the DOA estimation problem. Numerical simulations show that our proposed method overperforms traditional approaches, which is more close to the Cramer-Rao bound. |
| ArticleNumber | 104628 |
| Author | Qiao, Liyan Bai, Longxin Zhang, Jingchao |
| Author_xml | – sequence: 1 givenname: Longxin surname: Bai fullname: Bai, Longxin email: bailongxin@stu.hit.edu.cn – sequence: 2 givenname: Jingchao orcidid: 0000-0003-4501-6834 surname: Zhang fullname: Zhang, Jingchao email: zhangjingchao@hit.edu.cn – sequence: 3 givenname: Liyan surname: Qiao fullname: Qiao, Liyan email: qiaoliyan@hit.edu.cn |
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| Keywords | Direction-of-arrival estimation Nonconvex penalty The trimmed LASSO Recovery guarantee Majorization-minimization algorithm |
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| Title | A nonconvex sparse recovery method for DOA estimation based on the trimmed lasso |
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