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A Novel Parameter Determination Method for Lq Regularization Based Sparse SAR Imaging

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Machine Learning and Intelligent Communications (MLICOM 2017)

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Abstract

Sparse SAR imaging based on Lq(0 < q < 1) regularization has become a hot issue in SAR imaging. However, it can be difficult to determine a suitable value of the regularization parameter. In this paper, we developed a novel adaptive regularization parameter determination method for Lq regularization based SAR imaging. On the basis that the noise type in SAR system is mostly additive Gaussian white noise, we present a method for determining the regularization parameter through evaluating the statistics of noise. The parameter is updated through validating the statistical properties of the reconstruction error residuals in a suitable Noise Confidence Region (NCR). The experiment results illustrate the validity of the proposed method.

The authors would like to express thanks for the support of the Aeronautical Science Foundation (Grant No. 20151996016) and Coordinate Innovative Engineering Project of Shaanxi Province (Grant No. 2015KTTSGY0406).

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Authors and Affiliations

  1. Information and Navigation College, Air Force Engineering University, Xi’an, 710077, China

    Jia-cheng Ni, Qun Zhang & Li Sun

  2. Collaborative Innovation Center of Information Sensing and Understanding, Xi’an, 710077, China

    Qun Zhang

  3. Unit 95100 of PLA, Guangzhou, 510405, China

    Xian-jiao Liang

Authors
  1. Jia-cheng Ni
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  2. Qun Zhang
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  3. Li Sun
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  4. Xian-jiao Liang
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Corresponding author

Correspondence to Jia-cheng Ni .

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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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Ni, Jc., Zhang, Q., Sun, L., Liang, Xj. (2018). A Novel Parameter Determination Method for Lq Regularization Based Sparse SAR Imaging. 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_18

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  • DOI: https://doi.org/10.1007/978-3-319-73447-7_18

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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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