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DOA estimation for sparse nested MIMO radar with velocity receive sensor array

  • Jianfeng Li
  • Defu Jiang
  • Feng Wang
Article
  • 87 Downloads

Abstract

Direction of arrival (DOA) estimation for sparse nested MIMO radar with velocity receive sensor array is studied, and an algorithm based on extended unitary root multiple signal classification (MUSIC) is proposed. The nested MIMO radar utilizes sparse transmit array and velocity receive array with nested inter-element distances, which can make the final virtual array to be a long and sparse velocity sensor array. After exploiting unitary transformation to transform the data into real-valued one, an extended root MUSIC based method is developed to decompose the angle estimation into high-resolution but ambiguous and low-resolution but unambiguous DOA estimations, which are automatically paired. Thereafter, the ambiguous estimation is used to recover all possible DOAs, and the unambiguous DOA estimation is used as a reference to resolve the estimation ambiguity problem. Compared to conventional methods, the proposed algorithm requires no peak search, maintains larger aperture and achieves better DOA estimation performance. The simulation results verify the effectiveness of our approach.

Keywords

MIMO radar DOA estimation Sparse nested array Velocity receive sensor 

Notes

Acknowledgements

The authors would like to thank the reviewers for their helpful suggestions on improving the manuscript and thank the editor for his time spending the manuscript.

Funding This work is supported by National Natural Science Foundation of China (NSFC 61601167, 61371169), the Fundamental Research Funds for the Central Universities (2015B12614).

Author’s Contribution JL raised the idea and wrote the paper; DJ performed the experiments and analyzed the results; FW derived the CRB and helped to conduct the simulations.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Array and Information Processing Laboratory, College of Computer and InformationHohai UniversityNanjingChina

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