logo

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

More info
  • ReceivedMar 12, 2018
  • AcceptedMay 22, 2018
  • PublishedNov 27, 2018

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

[1] Zhang B C, Hong W, Wu Y R. Sparse microwave imaging: Principles and applications. Sci China Inf Sci, 2012, 55: 1722-1754 CrossRef Google Scholar

[2] Baraniuk R, Steeghs P. Compressive radar imaging. In: Proceedings of IEEE Radar Conference, Boston, 2007. 128--133. Google Scholar

[3] Jian Fang , Zongben Xu , Bingchen Zhang . Fast Compressed Sensing SAR Imaging Based on Approximated Observation. IEEE J Sel Top Appl Earth Observations Remote Sens, 2014, 7: 352-363 CrossRef ADS arXiv Google Scholar

[4] Bi H, Zhang B, Zhu X X. Extended Chirp Scaling-Baseband Azimuth Scaling-Based Azimuth-Range Decouple $L_{1}$ Regularization for TOPS SAR Imaging via CAMP. IEEE Trans Geosci Remote Sens, 2017, 55: 3748-3763 CrossRef ADS Google Scholar

[5] Quan X, Zhang B, Wang Z. An efficient data compression technique based on BPDN for scattered fields from complex targets. Sci China Inf Sci, 2017, 60: 109302 CrossRef Google Scholar

[6] Candes E, Tao T. The Dantzig selector: Statistical estimation when p is much larger than n. Ann Statist, 2007, 35: 2313-2351 CrossRef Google Scholar

[7] Selesnick I. Sparse Regularization via Convex Analysis. IEEE Trans Signal Process, 2017, 65: 4481-4494 CrossRef ADS arXiv Google Scholar

[8] Bauschke H H, Combettes P L. Convex Analysis and Monotone Operator Theory in Hilbert Spaces. New York: Springer, 2011. Google Scholar

[9] Naidu K. Data dome: full k-space sampling data for high-frequency radar research. Proc SPIE Int Soc Opt Eng, 2004, 5427: 200--207. Google Scholar

  •   

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

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1       京公网安备11010102003388号