DOA Estimation for Co-prime Array Based on Fast Sparse Bayesian Learning Using Bessel Priors

FENG Mingyue; HE Minghao; CHEN Changxiao; HAN Jun

doi:10.11999/JEIT170951

Volume 40 Issue 7

Jul. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(7): 1604-1611

FENG Mingyue, HE Minghao, CHEN Changxiao, HAN Jun. DOA Estimation for Co-prime Array Based on Fast Sparse Bayesian Learning Using Bessel Priors[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1604-1611. doi: 10.11999/JEIT170951

Citation:

FENG Mingyue, HE Minghao, CHEN Changxiao, HAN Jun. DOA Estimation for Co-prime Array Based on Fast Sparse Bayesian Learning Using Bessel Priors[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1604-1611. doi: 10.11999/JEIT170951

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DOA Estimation for Co-prime Array Based on Fast Sparse Bayesian Learning Using Bessel Priors

doi: 10.11999/JEIT170951

FENG Mingyue HE Minghao CHEN Changxiao HAN Jun

Funds:

The National Natural Science Foundation of China (61703430), The Natural Science Foundation of Hubei Province (2016CFB288)

Received Date: 2017-10-16
Rev Recd Date: 2018-01-16
Publish Date: 2018-07-19

Abstract

Abstract

In order to improve DOA estimation accuracy of co-prime array while the number of snapshots is small, a novel fast Sparse Bayesian Learning (SBL) algorithm using Bessel priors is proposed. Focusing on the multi-snapshots complex output data of coprime array, a multiple measurement vectors hierarchical model based on Bessel priors is firstly built. Then the log-likelihood function of model hyperparameters is derived, and the iterative formulas of hyperparameters are derived based on the criterion of maximum likelihood estimation. Finally, a fast implementation scheme is developed in order to improve the computation efficiency. Simulation experiments show that the proposed algorithm is independent on prior information. Under the condition of small number of snapshots, higher DOA estimation accuracy and resolution of uncorrelated and correlated signals can be achieved with proper computational efficiency. Further more, the necessity between virtual array extension and DOA estimation freedom of co-prime array is explored, which provides reference to DOA estimation for co-prime array under array perturbation conditions.
- Co-prime array、Direction Of Arrival (DOA) estimation、Sparse Bayesian Learning (SBL)、Bessel priors、Small number of snapshots,

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References(19)

References

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