The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans les problèmes d'optimisation basés sur la parcimonie pour l'estimation bidimensionnelle (2D) de la direction d'arrivée (DOA) à l'aide de tableaux imbriqués en forme de L, l'un des problèmes majeurs est la complexité de calcul. Un algorithme d'estimation DOA 2D est proposé, basé sur l'apprentissage bayésien clairsemé par reconstitution (RSBL) et la décomposition de matrice de covariance croisée. Un modèle à vecteur de mesure unique (SMV) est obtenu par le tableau de différences correspondant à un tableau imbriqué unidimensionnel. Grâce au lissage spatial, le vecteur de mesure du signal est transformé en une matrice de vecteurs de mesure multiples (MMV). La matrice de signaux est séparée par une décomposition en valeurs singulières (SVD) de la matrice. Grâce à cette méthode, la dimensionnalité de la matrice de détection et la taille des données peuvent être réduites. L'algorithme d'apprentissage bayésien clairsemé est utilisé pour estimer les angles unidimensionnels. En utilisant les estimations d'angle unidimensionnelles, la matrice de vecteurs de direction est reconstruite. La matrice de covariance croisée à deux dimensions est décomposée et transformée. Ensuite, l'expression fermée de la matrice vectorielle de direction d'une autre dimension est dérivée et les angles sont estimés. L'appairage automatique peut être réalisé en deux dimensions. Grâce à l'algorithme proposé, le problème de recherche 2D est transformé en un problème de recherche unidimensionnel et un problème de transformation matricielle. Les simulations montrent que l'algorithme proposé a une meilleure précision d'estimation d'angle que l'algorithme de radiogoniométrie bidimensionnel traditionnel avec un faible rapport signal/bruit et peu d'échantillons.
Lu CHEN
National University of Defense Technology
Daping BI
National University of Defense Technology
Jifei PAN
National University of Defense Technology
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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},}
Copier
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 -