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Spatial Spectrum Estimation for Wideband Signals by Sparse Reconstruction in Continuous Domain

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Advanced Hybrid Information Processing (ADHIP 2017)

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Abstract

A novel spatial spectrum estimation method for two-dimensional wideband signals by sparse reconstruction in continuous domain is addressed in this paper. First, Discrete Fourier Transform (DFT) is employed for the data. Then the convex and corresponding dual problems of the data with most power are founded and solved. After that the sparse support sets are decided by semidefinite program and extracting roots. Finally, both of the direction of arrival (DOA) and the primary signals are determined. The proposed idea averts the off-grid effect based on grid partition, and some theoretical results are included to explain the effectiveness of the method.

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 & Yanchao Li

Authors
  1. Jiaqi Zhen
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  2. Yanchao Li
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Correspondence to Jiaqi Zhen .

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Editors and Affiliations

  1. Harbin University of Science and Technology, Harbin, China

    Guanglu Sun

  2. Inner Mongolia University, Hohhot, China

    Shuai Liu

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

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Zhen, J., Li, Y. (2018). Spatial Spectrum Estimation for Wideband Signals by Sparse Reconstruction in Continuous Domain. In: Sun, G., Liu, S. (eds) Advanced Hybrid Information Processing. ADHIP 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-73317-3_43

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  • DOI: https://doi.org/10.1007/978-3-319-73317-3_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73316-6

  • Online ISBN: 978-3-319-73317-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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