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A Real-Valued Approximate Message Passing Algorithm for ISAR Image Reconstruction

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

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Abstract

Compressed sensing (CS) theory describes the signal using space transformation to obtain linear observation data selectively, breaking through the limit of the traditional Nyquist theorem. In this paper, we aim at accelerating the current approximate message passing (AMP) and propose an approach named real-valued AMP (RAMP) for faster and better inverse synthetic aperture radar (ISAR) imaging reconstruction. The azimuth dictionary is first processed with real. We then use matrix processing to solve the AMP vector iterative method, by utilizing the relation between the quantification of matrix product and the Kronecker product. The experimental results are presented to demonstrate the validity of this method.

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References

  1. Qiong-Hai D, Chang-Jun F, Xiang-yang J. Research on compressed sensing. Chin J Comput. 2011;34(3):425–34.

    Article  MathSciNet  Google Scholar 

  2. Kipnis A, Reeves G, Eldar YC, Compressed sensing under optimal quantization. In: IEEE international symposium on information theory (ISIT), Aachen; 2017. p. 2148–52.

    Google Scholar 

  3. Zheng L, Maleki A, Liu Q, An lp-based reconstruction algorithm for compressed sensing radar imaging. In: IEEE radar conference, Philadelphia; 2016. p. 1–5.

    Google Scholar 

  4. Donoho D. Compressed sensing. IEEE Trans Inf Theory. 2006;52(4):1289–306.

    Article  MathSciNet  Google Scholar 

  5. Zhu Q, Volz R, Mathews JD. Coherent radar imaging based on compressed sensing. Radio Sci. 2015;50(12):1271–85.

    Article  Google Scholar 

  6. Mohimani H, Babaie-Zadeh M, Jutten C. A fast approach for overcomplete sparse decomposition based on smoothed l0 norm. IEEE Trans Signal Process. 2009;57(1):289–301.

    Article  MathSciNet  Google Scholar 

  7. Tang L, Jiao S. Compressed sensing radar imaging using approximate message passing. CAAI Trans Intell Syst. 2015;10(4):592–8.

    Google Scholar 

  8. Maleki A, Anitori L, Yang Z. Asymptotic analysis of complex LASSO via complex approximate message passing (CAMP). IEEE Trans Inf Theory. 2013;59(7):4290–308.

    Article  MathSciNet  Google Scholar 

  9. Byrne E, Gribonval R, Schniter P, Sketched clustering via hybrid approximate message passing. In: Asilomar conference on signals, systems and computers, Pacific Grove; 2017. p. 410–4.

    Google Scholar 

  10. Patterson S, Eldar YC, Keidar I. Distributed compressed sensing for static and time-vary ing networks. IEEE Trans Signal Process. 2014;62(19):4931–46.

    Article  MathSciNet  Google Scholar 

  11. Musa O, Hannak G, Goertz N, Efficient recovery from noisy quantized compressed sensing using generalized approximate message passing. In: IEEE 7th international workshop on computational advances in multi-sensor adaptive processing (CAMSAP), Curacao; 2017. p. 1–5.

    Google Scholar 

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Acknowledgments

This work was supported in parts by the National Natural Science Foundation of China (no. 61301211), the Postgraduate Education Reform Project of Jiangsu Province (no. JGZZ17_008), and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (no. KYCX18_0295).

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Authors and Affiliations

  1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu, People’s Republic of China

    Wenyi Wei, Caiyun Wang & Panpan Huang

  2. Beijing Institute of Electronic System Engineering, Beijing, 100854, People’s Republic of China

    Jianing Wang, Xiaofei Li, Yuebin Sheng & Chunsheng Liu

Authors
  1. Wenyi Wei
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  2. Caiyun Wang
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  3. Jianing Wang
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  4. Xiaofei Li
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  5. Yuebin Sheng
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  6. Chunsheng Liu
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  7. Panpan Huang
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Corresponding author

Correspondence to Caiyun Wang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Wei, W. et al. (2020). A Real-Valued Approximate Message Passing Algorithm for ISAR Image Reconstruction. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_81

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  • DOI: https://doi.org/10.1007/978-981-13-6504-1_81

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

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