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Signal, Image and Video Processing

, Volume 13, Issue 7, pp 1311–1318 | Cite as

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

  • Yanhua Qin
  • Yumin LiuEmail author
  • Zhongyuan Yu
Original Paper

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.

Keywords

Coprime array Direction of arrival estimation Degrees of freedom Multiple measurement sparse Bayesian learning 

Notes

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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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Information Photonics and Optical CommunicationsBeijing University of Posts and TelecommunicationsBeijingChina

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