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2D DOA Estimation Based on Sparse Bayesian Learning for LShaped Nested Array
Lu CHEN Daping BI Jifei PAN
Publication
IEICE TRANSACTIONS on Communications
Vol.E102B
No.5
pp.992999 Publication Date: 2019/05/01
Online ISSN: 17451345
DOI: 10.1587/transcom.2018EBP3232
Type of Manuscript: PAPER Category: Fundamental Theories for Communications Keyword: twodimensional direction finding, nested array, sparse Bayesian learning, matrix decomposition, complexity reduction,
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Summary:
In sparsitybased optimization problems for two dimensional (2D) directionofarrival (DOA) estimation using Lshaped nested arrays, one of the major issues is computational complexity. A 2D DOA estimation algorithm is proposed based on reconsitution sparse Bayesian learning (RSBL) and cross covariance matrix decomposition. A single measurement vector (SMV) model is obtained by the difference coarray corresponding to onedimensional nested array. Through spatial smoothing, the signal measurement vector is transformed into a multiple measurement vector (MMV) matrix. The signal matrix is separated by singular values decomposition (SVD) of the matrix. Using this method, the dimensionality of the sensing matrix and data size can be reduced. The sparse Bayesian learning algorithm is used to estimate onedimensional angles. By using the onedimensional angle estimations, the steering vector matrix is reconstructed. The cross covariance matrix of two dimensions is decomposed and transformed. Then the closed expression of the steering vector matrix of another dimension is derived, and the angles are estimated. Automatic pairing can be achieved in two dimensions. Through the proposed algorithm, the 2D search problem is transformed into a onedimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional twodimensional direction finding algorithm at low signaltonoise ratio and few samples.

