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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Compressive sensing-based two-dimensional scattering-center extraction for incomplete RCS data

  • Bae, Ji-Hoon (Department of AI and Big Data Engineering, Daegu Catholic University) ;
  • Kim, Kyung-Tae (Electrical Engineering, Pohang University of Science and Technology)
  • Received : 2019.01.16
  • Accepted : 2020.01.08
  • Published : 2020.12.14
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Abstract

We propose a two-dimensional (2D) scattering-center-extraction (SCE) method using sparse recovery based on the compressive-sensing theory, even with data missing from the received radar cross-section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak-finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point-scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

Keywords

Acknowledgement

The authors would like to thank the associated editor and anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper.

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