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3 results on '"covariance matrix gridless sparse representation method"'

1. Gridless sparsity-based DOA estimation for sparse linear array

Author

Yu Zhang, Gong Zhang, Yingying Kong, and Fangqing Wen

Subjects
toeplitz matrices, array signal processing, eigenvalues and eigenfunctions, covariance matrices, direction-of-arrival estimation, gridless sparsity-based doa estimation, sparse linear array, two-stage gridless sparsity-based method, array direction-of-arrival estimation, covariance matrix gridless sparse representation method, atomic norm minimisation, corresponding structured toeplitz matrix construction, source number, conventional music algorithm, conventional subspace-based algorithms, two-stage method, Engineering (General). Civil engineering (General), TA1-2040
Abstract

A novel two-stage gridless sparsity-based method for sparse linear array direction-of-arrival (DOA) estimation is presented. First, the covariance matrix gridless sparse representation method based on atomic norm minimisation is proposed for corresponding structured Toeplitz matrix construction and source number detection. Then, the conventional MUSIC algorithm can be employed for DOA estimation. Compared with conventional subspace-based algorithms, the proposed two-stage method can be carried out without knowing source number and directly detect more source signals than sensors. Numerical simulations demonstrate the effectiveness and outperformance of the proposed method.

Published
2019
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2. Gridless sparsity-based DOA estimation for sparse linear array.

Author

Zhang, Yu, Zhang, Gong, Kong, Yingying, and Wen, Fangqing

Subjects
DIRECTION of arrival estimation, COVARIANCE matrices, MESHFREE methods, TOEPLITZ matrices, MULTIPLE Signal Classification, COMPUTER simulation
Abstract

A novel two-stage gridless sparsity-based method for sparse linear array direction-of-arrival (DOA) estimation is presented. First, the covariance matrix gridless sparse representation method based on atomic norm minimisation is proposed for corresponding structured Toeplitz matrix construction and source number detection. Then, the conventional MUSIC algorithm can be employed for DOA estimation. Compared with conventional subspace-based algorithms, the proposed two-stage method can be carried out without knowing source number and directly detect more source signals than sensors. Numerical simulations demonstrate the effectiveness and outperformance of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

3. Gridless sparsity‐based DOA estimation for sparse linear array

Author

Fangqing Wen, Yu Zhang, Gong Zhang, and Yingying Kong

Subjects
corresponding structured toeplitz matrix construction, gridless sparsity-based doa estimation, Computer science, Energy Engineering and Power Technology, 02 engineering and technology, conventional music algorithm, source number, Minimisation (clinical trials), Linear array, atomic norm minimisation, toeplitz matrices, 0203 mechanical engineering, conventional subspace-based algorithms, 0202 electrical engineering, electronic engineering, information engineering, Multiple signal classification, array signal processing, 020301 aerospace & aeronautics, two-stage method, Covariance matrix, Atomic norm, General Engineering, covariance matrices, 020206 networking & telecommunications, Sparse approximation, Toeplitz matrix, covariance matrix gridless sparse representation method, direction-of-arrival estimation, array direction-of-arrival estimation, lcsh:TA1-2040, two-stage gridless sparsity-based method, eigenvalues and eigenfunctions, sparse linear array, lcsh:Engineering (General). Civil engineering (General), Algorithm, Software, Subspace topology
Abstract

A novel two-stage gridless sparsity-based method for sparse linear array direction-of-arrival (DOA) estimation is presented. First, the covariance matrix gridless sparse representation method based on atomic norm minimisation is proposed for corresponding structured Toeplitz matrix construction and source number detection. Then, the conventional MUSIC algorithm can be employed for DOA estimation. Compared with conventional subspace-based algorithms, the proposed two-stage method can be carried out without knowing source number and directly detect more source signals than sensors. Numerical simulations demonstrate the effectiveness and outperformance of the proposed method.

Published
2019

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