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      High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo Denoising

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          Abstract

          This study proposes a high-resolution radar imaging method combined with the sparse low-rank matrix recovery technique and the deconvolution algorithm based on the matched filtering result. We establish a two-Dimensional (2D) convolution model for the radar signal after the Matched Filter (MF) to maximize the Signal-to-Noise Ratio (SNR) and use the 2D deconvolution algorithm of the Wiener filter to obtain a high resolution. However, representative deconvolution algorithms are confronted with an ill-posed problem, which magnifies the noise while influencing the imaging resolution. Prior to this study, the echo matrix after the MF was proven to be sparse and low rank under the constraint of a sparsely distributed target. The target distribution is smoothed by the influence of the point spread function. Hence, inspired by these points, we further enhance the SNR of the echo matrix based on the sparse and low-rank characteristics to alleviate the illposed problem of deconvolution and the poor resolution of the Wiener filter. The performance of the proposed method is demonstrated by the real experimental data.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 June 2018
          : 7
          : 3
          : 285-293
          Affiliations
          [1 ] ①(Department of EEIS, University of Science and Technology of China, Hefei 230027, China) ②(Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230027, China)
          [2 ] ③(Academy of Photoelectric Technology, Hefei University of Technology, Hefei 230009, China)
          Article
          9c82f3f4d20e493699cc3d2660ccc778
          10.12000/JR17108

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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