High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo Denoising
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 ma...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2018-06-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | http://radars.ie.ac.cn/fileup/HTML/R17108.htm |
| _version_ | 1797426937644187648 |
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| author | Lu Xinfei Xia Jie Yin Zhiping Chen Weidong |
| author_facet | Lu Xinfei Xia Jie Yin Zhiping Chen Weidong |
| author_sort | Lu Xinfei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T08:37:04Z |
| format | Article |
| id | doaj.art-9c82f3f4d20e493699cc3d2660ccc778 |
| institution | Directory Open Access Journal |
| issn | 2095-283X 2095-283X |
| language | English |
| last_indexed | 2024-03-09T08:37:04Z |
| publishDate | 2018-06-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-9c82f3f4d20e493699cc3d2660ccc7782023-12-02T17:53:16ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2095-283X2018-06-017328529310.12000/JR17108High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo DenoisingLu Xinfei0Xia Jie1Yin Zhiping2Chen Weidong3①(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)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)③(Academy of Photoelectric Technology, Hefei University of Technology, Hefei 230009, China)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)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.http://radars.ie.ac.cn/fileup/HTML/R17108.htmHigh resolution radar imagingEcho denosingTwo-dimensional deconvolutionLow rank matrix recovery |
| spellingShingle | Lu Xinfei Xia Jie Yin Zhiping Chen Weidong High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo Denoising Leida xuebao High resolution radar imaging Echo denosing Two-dimensional deconvolution Low rank matrix recovery |
| title | High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo Denoising |
| title_full | High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo Denoising |
| title_fullStr | High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo Denoising |
| title_full_unstemmed | High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo Denoising |
| title_short | High-resolution Radar Imaging Using 2D Deconvolution with Sparse Echo Denoising |
| title_sort | high resolution radar imaging using 2d deconvolution with sparse echo denoising |
| topic | High resolution radar imaging Echo denosing Two-dimensional deconvolution Low rank matrix recovery |
| url | http://radars.ie.ac.cn/fileup/HTML/R17108.htm |
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