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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Bibliographic Details
Published in:Digital signal processing Vol. 153; p. 104628
Main Authors: Bai, Longxin, Zhang, Jingchao, Qiao, Liyan
Format: Journal Article
Language:English
Published: Elsevier Inc 01.10.2024
Subjects:
Direction-of-arrival estimation
Majorization-minimization algorithm
Nonconvex penalty
Recovery guarantee
The trimmed LASSO
Direction-of-arrival estimation
Nonconvex penalty
The trimmed LASSO
Recovery guarantee
Majorization-minimization algorithm
ISSN:1051-2004, 1095-4333
Online Access:Get full text
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Summary: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.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2024.104628