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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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 72; no. 11; pp. 1 - 14
Main Authors: Liu, Donghe, Zhao, Yongbo
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary: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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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2023.3284915