2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped Nested Array

Lu CHEN; Daping BI; Jifei PAN

doi:10.1587/transcom.2018EBP3232

IEICE TRANSACTIONS on Communications

2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped Nested Array

Lu CHEN, Daping BI, Jifei PAN

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In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D 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 one-dimensional 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 one-dimensional angles. By using the one-dimensional 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 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.

Publication: IEICE TRANSACTIONS on Communications Vol.E102-B No.5 pp.992-999

Publication Date: 2019/05/01

Publicized: 2018/10/23

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2018EBP3232

Type of Manuscript: PAPER

Category: Fundamental Theories for Communications

Authors

Lu CHEN
  National University of Defense Technology
Daping BI
  National University of Defense Technology
Jifei PAN
  National University of Defense Technology

Keyword

two-dimensional direction finding, nested array, sparse Bayesian learning, matrix decomposition, complexity reduction

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Lu CHEN, Daping BI, Jifei PAN, "2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped Nested Array" in IEICE TRANSACTIONS on Communications, vol. E102-B, no. 5, pp. 992-999, May 2019, doi: 10.1587/transcom.2018EBP3232.
Abstract: In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D 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 one-dimensional 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 one-dimensional angles. By using the one-dimensional 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 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3232/_p

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@ARTICLE{e102-b_5_992,
author={Lu CHEN, Daping BI, Jifei PAN, },
journal={IEICE TRANSACTIONS on Communications},
title={2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped Nested Array},
year={2019},
volume={E102-B},
number={5},
pages={992-999},
abstract={In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D 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 one-dimensional 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 one-dimensional angles. By using the one-dimensional 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 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.},
keywords={},
doi={10.1587/transcom.2018EBP3232},
ISSN={1745-1345},
month={May},}

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TY - JOUR
TI - 2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped Nested Array
T2 - IEICE TRANSACTIONS on Communications
SP - 992
EP - 999
AU - Lu CHEN
AU - Daping BI
AU - Jifei PAN
PY - 2019
DO - 10.1587/transcom.2018EBP3232
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2019
AB - In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D 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 one-dimensional 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 one-dimensional angles. By using the one-dimensional 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 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.
ER -

IEICE TRANSACTIONS on Communications