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Direction of Arrive Estimation in Spherical Harmonic Domain Using Super Resolution Approach

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Machine Learning and Intelligent Communications (MLICOM 2017)

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Abstract

Spherical array plays important role in 3D targets localization. In this paper, we develop a novel DOA estimation method for the spherical array with super resolution approach. The proposed method operates in spherical harmonic domain. Based on the atomic norm minimization, we develop a gridless L1-SVD algorithm in spherical harmonic domain and then we adopt the spherical ESPRIT method to two-dimensional DOA estimation. Compared to the previous work, the proposed method acquires better estimation performance. Numerical simulation results verify the performance of the proposed method.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61601402), Jiangsu Province Science Foundation of China (BK20160477).

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Authors and Affiliations

  1. College of Information Engineering, Yangzhou University, Yangzhou, 221000, China

    Jie Pan, Yalin Zhu & Changling Zhou

Authors
  1. Jie Pan
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  2. Yalin Zhu
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  3. Changling Zhou
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Corresponding author

Correspondence to Jie Pan .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Pan, J., Zhu, Y., Zhou, C. (2018). Direction of Arrive Estimation in Spherical Harmonic Domain Using Super Resolution Approach. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_21

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  • DOI: https://doi.org/10.1007/978-3-319-73447-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73446-0

  • Online ISBN: 978-3-319-73447-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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