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SCIENCE CHINA Information Sciences
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SCIENCE CHINA Information Sciences, Volume 62 , Issue 2 : 029305(2019) https://doi.org/10.1007/s11432-018-9464-4

A SAR imaging method based on generalized minimax-concave penalty

Zhonghao WEI 1,2,3,*, Bingchen ZHANG 1,3, Yirong WU 3
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  • ReceivedMar 12, 2018
  • AcceptedMay 22, 2018
  • PublishedNov 27, 2018
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Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61571419).


Supplement

Figures A1, B1, B2, C1, C2.


References

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    Algorithm 1 Forward-backward algorithm for GMC based SAR imaging

    Input: Echo data $\boldsymbol{y}\in~\mathbb{C}^N$, measurement matrix $\boldsymbol{\Phi}\in~\mathbb{C}^{M\times~N}$, and number of the targets $K$;

    Initialization: $0.5\leq\gamma\leq0.8$,$\rho~=~\max~\{1,\gamma~/~(1-\gamma)\}\|\boldsymbol{\Phi}^{\rm~H}\boldsymbol{\Phi}\|$,$\zeta$: $0<\zeta<2/\rho$.

    Iteration:

    for $~i~=~1:I$

    $\boldsymbol{w}^{i}~=~\boldsymbol{x}^{i}~-~\zeta~{\boldsymbol{\Phi}}^{\rm~H}(\boldsymbol{\Phi}(\boldsymbol{x}^{i}~+~\gamma(\boldsymbol{v}^i~-~\boldsymbol{x}^i))-\boldsymbol{y})$;

    $\boldsymbol{\mu}~^i~=~v^i~-~\zeta~\gamma~{\boldsymbol{\Phi}}^{\rm~H}(\boldsymbol{\Phi}(\boldsymbol{v}^i~-~\boldsymbol{x}^i))$;

    $\lambda~=~|\boldsymbol{w}^{i}|_{K+1}/\zeta$;

    $\boldsymbol{x}^{i}~=~f_{\lambda\zeta}(\boldsymbol{w}^i)$;

    $\boldsymbol{v}^{i}~=~f_{\lambda\zeta}(\boldsymbol{\mu}~^i)$;

    end for

    Output: $\boldsymbol{x}=\boldsymbol{x}^{i}.$

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