Two-Dimensional DOA estimation via matrix completion and sparse matrix recovery for coprime planar array
The coprime planar array (CPPA) can obtain a larger virtual aperture using the sum-difference co-array (SDCA). Nonetheless, the holes in the SDCA always cause the virtual aperture to be not fully utilized. In order to solve this issue, a two-dimensional (2-D) directional of arrival (DOA) estimation...
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| Vydáno v: | IEEE transactions on vehicular technology Ročník 72; číslo 11; s. 1 - 14 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9545, 1939-9359 |
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| Abstract | The coprime planar array (CPPA) can obtain a larger virtual aperture using the sum-difference co-array (SDCA). Nonetheless, the holes in the SDCA always cause the virtual aperture to be not fully utilized. In order to solve this issue, a two-dimensional (2-D) directional of arrival (DOA) estimation algorithm with CPPA via matrix completion and sparse matrix recovery is proposed in this paper. To accurately complete the missing elements in the SDCA, we construct an optimization problem based on the truncated nuclear norm regularization (TNNR) by constraining the conjugate flip symmetry property of the virtual array and the noise term. Moreover, we derive a complex-valued sparse matrix recovery algorithm based on the fast iterative shrinkage-thresholding (FISTA) method avoiding using Kronecker product operations between dictionary matrices, which aims to reduce the computational complexity of the conventional vector-form sparse recovery algorithms. Therefore, the proposed algorithm can achieve a larger virtual aperture and lower computational complexity, improving the angle estimation performance. Simulation results demonstrate the effectiveness of the proposed algorithm for CPPA. |
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| AbstractList | The coprime planar array (CPPA) can obtain a larger virtual aperture using the sum-difference co-array (SDCA). Nonetheless, the holes in the SDCA always cause the virtual aperture to be not fully utilized. In order to solve this issue, a two-dimensional (2-D) directional of arrival (DOA) estimation algorithm with CPPA via matrix completion and sparse matrix recovery is proposed in this paper. To accurately complete the missing elements in the SDCA, we construct an optimization problem based on the truncated nuclear norm regularization (TNNR) by constraining the conjugate flip symmetry property of the virtual array and the noise term. Moreover, we derive a complex-valued sparse matrix recovery algorithm based on the fast iterative shrinkage-thresholding (FISTA) method avoiding using Kronecker product operations between dictionary matrices, which aims to reduce the computational complexity of the conventional vector-form sparse recovery algorithms. Therefore, the proposed algorithm can achieve a larger virtual aperture and lower computational complexity, improving the angle estimation performance. Simulation results demonstrate the effectiveness of the proposed algorithm for CPPA. The coprime planar array (CPPA) can obtain a larger virtual aperture using the sum-difference co-array (SDCA). Nonetheless, the holes in the SDCA always cause the virtual aperture to be not fully utilized. In order to solve this issue, a two-dimensional (2-D) directional of arrival (DOA) estimation algorithm with CPPA via matrix completion and sparse matrix recovery is proposed in this article. To accurately complete the missing elements in the SDCA, we construct an optimization problem based on the truncated nuclear norm regularization (TNNR) by constraining the conjugate flip symmetry property of the virtual array and the noise term. Moreover, we derive a complex-valued sparse matrix recovery algorithm based on the fast iterative shrinkage-thresholding (FISTA) method avoiding using Kronecker product operations between dictionary matrices, which aims to reduce the computational complexity of the conventional vector-form sparse recovery algorithms. Therefore, the proposed algorithm can achieve a larger virtual aperture and lower computational complexity, improving the angle estimation performance. Simulation results demonstrate the effectiveness of the proposed algorithm for CPPA. |
| Author | Liu, Donghe Zhao, Yongbo |
| Author_xml | – sequence: 1 givenname: Donghe orcidid: 0000-0002-3190-3806 surname: Liu fullname: Liu, Donghe organization: Donghe Liu and Yongbo Zhao are with the National Key Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi'an, China – sequence: 2 givenname: Yongbo orcidid: 0000-0002-6453-0786 surname: Zhao fullname: Zhao, Yongbo organization: Donghe Liu and Yongbo Zhao are with the National Key Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi'an, China |
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| SubjectTerms | Algorithms Apertures Arrays Complexity Computational complexity Coprime planar array (CPPA) Direction-of-arrival estimation Estimation Iterative methods Mathematical analysis matrix completion Optimization Recovery Regularization Signal processing algorithms Sparse matrices sparse matrix recovery Sparsity Transmission line matrix methods two-dimensional directional of arrival (DOA) estimation |
| Title | Two-Dimensional DOA estimation via matrix completion and sparse matrix recovery for coprime planar array |
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