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Underdetermined DOA estimation using coprime array via multiple measurement sparse Bayesian learning

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Abstract

Underdetermined direction of arrival (DOA) estimation with coprime array is discussed in the framework of multiple measurement sparse Bayesian learning (MSBL). Exploiting the extended difference coarray, a larger number of degrees of freedom can be obtained for locating more sources than sensors. A linear operation and a prewhitening procedure are incorporated into the sparse signal recovery model to eliminate the influence of noise. Then, MSBL employs an empirical Bayesian strategy to resolve \(l_{0}\) minimization problem. Simulation results show the superiority of the MSBL algorithm in underdetermined DOA detection performance, resolution ability and estimation accuracy when there are multiple measurement vectors for on-grid and off-grid sources, respectively.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their many insightful comments and suggestions, which help improve the quality and readability of this paper.

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Correspondence to Yumin Liu.

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Qin, Y., Liu, Y. & Yu, Z. Underdetermined DOA estimation using coprime array via multiple measurement sparse Bayesian learning. SIViP 13, 1311–1318 (2019). https://doi.org/10.1007/s11760-019-01480-x

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