Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/175581
Title: DOA Estimation of Time-Modulated Linear Array Based on Sparse Signal Recovery
Authors: Wen Tao Li;Ya Jie Lei;Xiao Wei Shi
Year: 2017
Publisher: IEEE
Abstract: Under the circumstances of small number of snapshots, low signal-to-noise ratio, and closely spaced sources, especially with correlated signals, the existing direction of arrival angle (DOA) estimation methods for time-modulated linear arrays (TMLAs) generally does not yield satisfactory results. To deal with those problems, a new weighted <inline-formula><tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm DOA estimation algorithm is proposed for the TMLA in this letter. The proposed algorithm constructs the weighted matrix by making full use of the orthogonality of the signal subspace and the noise subspace to penalize the <inline-formula> <tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm constrained model. Accordingly, the reconstructed coefficient vector with better sparsity could be achieved, and the false peaks could be effectively suppressed. Simulation results have been provided to validate the effectiveness of the proposed method.
Description: 
URI: http://localhost/handle/Hannan/175581
volume: 16
More Information: 2336,
2340
Appears in Collections:2017

Files in This Item:
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7954768.pdf527.29 kBAdobe PDF
Title: DOA Estimation of Time-Modulated Linear Array Based on Sparse Signal Recovery
Authors: Wen Tao Li;Ya Jie Lei;Xiao Wei Shi
Year: 2017
Publisher: IEEE
Abstract: Under the circumstances of small number of snapshots, low signal-to-noise ratio, and closely spaced sources, especially with correlated signals, the existing direction of arrival angle (DOA) estimation methods for time-modulated linear arrays (TMLAs) generally does not yield satisfactory results. To deal with those problems, a new weighted <inline-formula><tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm DOA estimation algorithm is proposed for the TMLA in this letter. The proposed algorithm constructs the weighted matrix by making full use of the orthogonality of the signal subspace and the noise subspace to penalize the <inline-formula> <tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm constrained model. Accordingly, the reconstructed coefficient vector with better sparsity could be achieved, and the false peaks could be effectively suppressed. Simulation results have been provided to validate the effectiveness of the proposed method.
Description: 
URI: http://localhost/handle/Hannan/175581
volume: 16
More Information: 2336,
2340
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7954768.pdf527.29 kBAdobe PDF
Title: DOA Estimation of Time-Modulated Linear Array Based on Sparse Signal Recovery
Authors: Wen Tao Li;Ya Jie Lei;Xiao Wei Shi
Year: 2017
Publisher: IEEE
Abstract: Under the circumstances of small number of snapshots, low signal-to-noise ratio, and closely spaced sources, especially with correlated signals, the existing direction of arrival angle (DOA) estimation methods for time-modulated linear arrays (TMLAs) generally does not yield satisfactory results. To deal with those problems, a new weighted <inline-formula><tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm DOA estimation algorithm is proposed for the TMLA in this letter. The proposed algorithm constructs the weighted matrix by making full use of the orthogonality of the signal subspace and the noise subspace to penalize the <inline-formula> <tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm constrained model. Accordingly, the reconstructed coefficient vector with better sparsity could be achieved, and the false peaks could be effectively suppressed. Simulation results have been provided to validate the effectiveness of the proposed method.
Description: 
URI: http://localhost/handle/Hannan/175581
volume: 16
More Information: 2336,
2340
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7954768.pdf527.29 kBAdobe PDF