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Gain-Phase Error Calculation in DOA Estimation for Mixed Wideband Signals

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Machine Learning and Intelligent Communications (MLICOM 2017)

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Abstract

Gain-phase error is inevitable in direction of arrival (DOA) estimation, it will lead to the mismatch between actual and ideal array manifold. Therefore, a novel gain-phase error calculation approach in DOA estimation for mixed wideband signals is provided in this paper. First, the signals are transformed on the focusing frequency. Then peak searching is employed for determining the far-field sources. Finally, gain-phase error can be calculated according to the orthogonality of far-field signal subspace and noise subspace, simulation results manifest the effectiveness of the proposed approach.

J. Zhen—This work was supported by the National Natural Science Foundation of China under Grant No. 61501176 and 61505050, University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCT-2016017), China Postdoctoral Science Foundation (2014M561381), Heilongjiang Province Postdoctoral Foundation (LBH-Z14178), Heilongjiang Province Natural Science Foundation (F2015015), Outstanding Young Scientist Foundation of Heilongjiang University (JCL201504) and Special Research Funds for the Universities of Heilongjiang Province (HDRCCX-2016Z10).

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Authors and Affiliations

  1. College of Electronic Engineering, Heilongjiang University, Harbin, 150080, China

    Jiaqi Zhen, Yong Liu & Yanchao Li

Authors
  1. Jiaqi Zhen
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  2. Yong Liu
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  3. Yanchao Li
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Correspondence to Jiaqi Zhen .

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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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Zhen, J., Liu, Y., Li, Y. (2018). Gain-Phase Error Calculation in DOA Estimation for Mixed Wideband Signals. 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 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_60

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  • DOI: https://doi.org/10.1007/978-3-319-73564-1_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73563-4

  • Online ISBN: 978-3-319-73564-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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