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Bistatic ISAR Radar Imaging Using Missing Data Based on Compressed Sensing

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Communications, Signal Processing, and Systems (CSPS 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 571))

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Abstract

When a bistatic inverse synthetic aperture radar (ISAR) system fails to collect complete radar cross section (RCS) datasets, bistatic ISAR images are usually corrupted using the conventional Fourier transform (FT)-based imaging algorithm. To overcome this problem, this paper proposes a new bistatic ISAR image reconstruction method that includes three steps: construction of the sparse dictionaries according to the range and cross resolution units on the imaging domain and echoes can be considered as the interaction between the two-dimensional distribution of point scatterers and the sparse dictionary, construction of the observation matrix and low-dimensional observation samples are obtained, and reconstruction of scattering distribution of target using nonlinear reconstruction algorithm. To validate the reconstruction capability of the proposed method, bistatic-scattered field data using the point-scatterer model is used for bistatic ISAR image reconstruction. The results show that the proposed imaging method based on the bistatic ISAR signal model spatial reconstruction combined with the compressive sensing(CS) theory can yield high reconstruction accuracy for incomplete bistatic RCS data compared to conventional FT-based imaging methods.

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Acknowledgements

The authors would like to thank Professor Yiming Pi for sharing his expertise on ISAR and compressed sensing. This work was supported by the Science and Technology Department of Yibin under Grants 2018ZSF001 and the Science and Technology Department of Sichuan Province under Grants 2018JZ0050.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China

    Luhong Fan, Zongjie Cao, Jin Li, Rui Min & Zongyong Cui

Authors
  1. Luhong Fan
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  2. Zongjie Cao
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  3. Jin Li
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  4. Rui Min
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  5. Zongyong Cui
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Corresponding author

Correspondence to Luhong Fan .

Editor information

Editors and Affiliations

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

    Qilian Liang

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

    Wei Wang

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

    Xin Liu

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

    Zhenyu Na

  5. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China

    Min Jia

  6. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Fan, L., Cao, Z., Li, J., Min, R., Cui, Z. (2020). Bistatic ISAR Radar Imaging Using Missing Data Based on Compressed Sensing. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_138

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  • DOI: https://doi.org/10.1007/978-981-13-9409-6_138

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9408-9

  • Online ISBN: 978-981-13-9409-6

  • eBook Packages: EngineeringEngineering (R0)

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