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Accelerating near-field 3D imaging approach for joint high-resolution imaging and phase error correction

  • Yang FangEmail author
  • Baoping Wang
  • Chao Sun
  • Zuxun Song
  • Shuzhen Wang
Article
  • 177 Downloads

Abstract

The computational complexity and memory requirements of large-scale data seriously affect the application of compressed sensing (CS) in near-field three-dimensional (3-D) imaging system. In addition, as influenced by the measurement environment, the error in echo phase results in imaging defocusing. This paper proposes a CS near-field 3-D imaging approach based on nonuniform fast Fourier transform and phase error correction. It applies the fast Gaussian gridding nonuniform fast Fourier transform technique and Separable Surrogate Functionals with only matrix and vector multiplied to accelerate imaging speed and reduce memory requirements; it adopts the phase error correction technique to realize highly-focused imaging; in addition, a sparse observation approach based on Logistic sequence is proposed in this paper for easy availability of engineering realization for CS imaging. As indicated by numerical analysis and actual measurement in anechoic chamber, the approach proposed in this paper, compared with traditional imaging approaches, has the following advantages: accurate high resolution 3-D image of target can be obtained by applying small amount of observation data (10%); the computational complexity falls from O(LN) to O(3N) and memory occupation quantity drops from O(LN) to O(N); it can effectively perform highly-focused imaging for echo signal with phase error; the measurement matrix designed has better non-coherence and easy availability for engineering realization.

Keywords

Near-field 3-D imaging Compressed sensing (CS) Fast Gaussian gridding nonuniform fast Fourier transform (FGG-NUFFT) Phase error Chaotic measurement matrix Planar scanning 

Notes

Acknowledgements

This research is being supported by the National Natural Science Foundation of China under Grant 61472324, 61771369. The authors would like to thank the journal manager, the handling editor and the anonymous reviewers for their valuable and helpful comments.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Yang Fang
    • 1
    Email author
  • Baoping Wang
    • 2
  • Chao Sun
    • 1
  • Zuxun Song
    • 1
  • Shuzhen Wang
    • 3
  1. 1.School of Electronics and InformationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.National Key Laboratory of Science and Technology on UAVNorthwestern Polytechnical UniversityXi’anChina
  3. 3.School of Computer Science and TechnologyXidian UniversityXi’anChina

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