l1 Norm Minimization Method Based on Data Reconstruction in MIMO Radar" /> l1 norm minimization method based on data reconstruction in monostatic multiple-input multiple-output (MIMO) radar is proposed. Exploiting the special structure of the received data, and through the received data reconstruction approach and unitary transformation technique, a one-dimensional real-valued received data matrix can be obtained for recovering the sparse signal. Then a weight matrix based on real-valued MUSIC spectrum is designed for reweighting l1 norm minimization to enhance the sparsity of solution. Finally, the DOA can be estimated by finding the non-zero rows in the recovered matrix. Compared with traditional l1 norm-based minimization methods, the proposed method provides better angle estimation performance. Simulation results are presented to verify the effectiveness and advantage of the proposed method." /> l1 Norm Minimization Method Based on Data Reconstruction in MIMO Radar" />
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Real-Valued Reweighted l1 Norm Minimization Method Based on Data Reconstruction in MIMO Radar

Qi LIU
Wei WANG
Dong LIANG
Xianpeng WANG

Publication
IEICE TRANSACTIONS on Communications   Vol.E98-B    No.11    pp.2307-2313
Publication Date: 2015/11/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E98.B.2307
Type of Manuscript: PAPER
Category: Antennas and Propagation
Keyword: 
MIMO radar,  DOA estimation,  sparse representation,  real-valued reweighted l1 norm minimization,  

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Summary: 
In this paper, a real-valued reweighted l1 norm minimization method based on data reconstruction in monostatic multiple-input multiple-output (MIMO) radar is proposed. Exploiting the special structure of the received data, and through the received data reconstruction approach and unitary transformation technique, a one-dimensional real-valued received data matrix can be obtained for recovering the sparse signal. Then a weight matrix based on real-valued MUSIC spectrum is designed for reweighting l1 norm minimization to enhance the sparsity of solution. Finally, the DOA can be estimated by finding the non-zero rows in the recovered matrix. Compared with traditional l1 norm-based minimization methods, the proposed method provides better angle estimation performance. Simulation results are presented to verify the effectiveness and advantage of the proposed method.