Covariance matrix reconstruction algorithm based on minimum atomic norm for coprime array
Covariance matrix reconstruction algorithm based on minimum atomic norm for coprime array
- Author(s): G. Chen 1 and X. Luo 1
- DOI: 10.1049/icp.2021.0783
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- Author(s): G. Chen 1 and X. Luo 1
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View affiliations
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Affiliations:
1:
School of Information Engineering, Nanchang Institute of Technology , Nanchang , China
Source:
IET International Radar Conference (IET IRC 2020),
2021
p.
705 – 708
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Affiliations:
1:
School of Information Engineering, Nanchang Institute of Technology , Nanchang , China
- Conference: IET International Radar Conference (IET IRC 2020)
- DOI: 10.1049/icp.2021.0783
- ISBN: 978-1-83953-540-6
- Location: Online Conference
- Conference date: 04-06 November 2020
- Format: PDF
To solve the problem that Direction of Arrival(DOA) estimation based on virtual array interpolation does not fully utilize covariance matrix information, the reconstruction of covariance matrix of coprime array is transformed into low-rank matrix completion and atomic norm optimization, and proposes the reconstruction algorithm of covariance matrix of mutual array based on atomic norm.In this algorithm, the Toeplitz covariance matrix is expressed as the Toeplitz completion problem by the generalized augmented method, and then the truncated singular value threshold(SVT) method is used as the reference in the atomic norm optimization problem to carry out the optimization solution and transformed to achieve fast positive definite Toeplitz covariance matrix reconstruction. The algorithm makes full use of the information contained in the coprime array to improve the stability of DOA estimation algorithm and reduce the computational complexity.
Inspec keywords: array signal processing; singular value decomposition; covariance matrices; direction-of-arrival estimation; computational complexity; interpolation; Toeplitz matrices
Subjects: Optimisation techniques; Signal processing and detection; Interpolation and function approximation (numerical analysis); Optimisation techniques; Other topics in statistics; Computational complexity; Signal processing theory; Linear algebra (numerical analysis); Linear algebra (numerical analysis); Interpolation and function approximation (numerical analysis); Other topics in statistics