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

HAN Jun; HE Minghao; FENG Mingyue; JIANG Ying

doi:10.1049/cje.2019.05.012

Volume 28 Issue 4

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Article Navigation > Chinese Journal of Electronics > 2019 > 28(4): 863-870

HAN Jun, HE Minghao, FENG Mingyue, et al., “CFAR Block-Sparse Bayesian Learning Algorithm for the Off-grid DOA Estimation with Coprime Array,” Chinese Journal of Electronics, vol. 28, no. 4, pp. 863-870, 2019, doi: 10.1049/cje.2019.05.012

Citation:

HAN Jun, HE Minghao, FENG Mingyue, et al., “CFAR Block-Sparse Bayesian Learning Algorithm for the Off-grid DOA Estimation with Coprime Array,” Chinese Journal of Electronics, vol. 28, no. 4, pp. 863-870, 2019, doi: 10.1049/cje.2019.05.012

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

doi: 10.1049/cje.2019.05.012

Air Force Early Warning Academy, Wuhan 430019, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61401504), Natural Foundation of Hubei Province (No.2016CFB288), and the Military Plan of Scientific Research Project.

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Corresponding author: FENG Mingyue (corresponding author) was born in 1988. He received the B.S. degree and M.S. degree from Air Force Radar Academy, Wuhan, China in 2011 and 2014 respectively, and received the Ph.D. degree from Air Force Radar cademy Wuhan, China in 2018. His interests include array signal processing and electronic countermeasures. (Email:fengmingyue2005@163.com)
Received Date: 2018-01-29
Rev Recd Date: 2018-08-21
Publish Date: 2019-07-10

Abstract

Abstract

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 estimation,
- Block sparse Bayesian learning (BSBL),
- Coprime array,
- CFAR detection,
- Low complexity

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References(21)

References

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