首页|CFAR Block-Sparse Bayesian Learning Algorithm for the Off-grid DOA Estimation with Coprime Array

CFAR Block-Sparse Bayesian Learning Algorithm for the Off-grid DOA Estimation with Coprime Array

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Constant false-alarm rate Block-sparse Bayesian learning (CFAR-BSBL) algorithm is proposed to reduce the computational complexity and improve Direction of arrival (DOA) estimation accuracy of offgrid signals with coprime array. Firstly, a signal model with normalized noise is built to avoid the learning procedure of noise parameter. Secondly, a block sparse Bayesian framework is built with the introduction of a temporary correlation matrix in order to use t he temporal structure of incident signals. Then the algorithm uses CFAR detection to detect the grids close to the real DOA and relieve the dependence on the number of signals. Finally, an off-grid process based on the closest grids is adopted to deal with the off-grid problem. The proposed CFAR-BSBL algorithm can obtain high accuracy and low complexity DOA estimation of off-grid signals with coprime array.

Off-grid direction-of-arrival estimationBlock sparse Bayesian learning (BSBL)Coprime arrayCFAR detectionLow complexity

HAN Jun、HE Minghao、FENG Mingyue、JIANG Ying

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Air Force Early Warning Academy, Wuhan 430019, China

This work is supported by the National Natural Science Foundation of ChinaNatural Foundation of Hubei ProvinceMilitary Plan of Scientific Research Project

614015042016CFB288

2019

10.1049/cje.2019.05.012

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(4)
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