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

Lu Xinfei; Xia Jie; Yin Zhiping; Chen Weidong

doi:10.12000/JR17108

Volume 7 Issue 3

Jul. 2018

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Article Navigation > Journal of Radars > 2018 > 7(3): 285-293

LIU Pingyu, LYU Xiaode, LIU Zhongsheng, et al. Research on co-channel interference suppression method for passive radar based on the jiont processing of primary and reference channels[J]. Journal of Radars, 2020, 9(6): 974–986. doi: 10.12000/JR19047

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Lu Xinfei, Xia Jie, Yin Zhiping, et al.. High-resolution radar imaging using 2D deconvolution with sparse echo denoising[J]. Journal of Radars, 2018, 7(3): 285–293. DOI: 10.12000/JR17108

Citation:

Lu Xinfei, Xia Jie, Yin Zhiping, et al.. High-resolution radar imaging using 2D deconvolution with sparse echo denoising[J]. Journal of Radars, 2018, 7(3): 285–293. DOI: 10.12000/JR17108

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

DOI: 10.12000/JR17108

Lu Xinfei^{1, 2
,},
Xia Jie^{1, 2
,},
Yin Zhiping^3
,,
Chen Weidong^{1, 2
,
,}

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

Funds: The National Natural Science Foundation of China (61401140).

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Author Bio:
Lu Xinfei (1990–) was born in Anhui, China. He received the B.E., M.E. and Ph.D. degrees both from University of Science and Technology of China (USTC), Hefei, China, in 2011, 2015 and 2017, respectively. His current research interests include MIMO imaging, ISAR imaging, compressed sensing, and signal reconstruction. E-mail: lxfei@mail.ustc.edu.cn

Xia Jie (1993–) was born in Anhui, China. She is currently a master student in University of Science and Technology of China. She received her Bachelor degree in electronic engineering and information science from HeFei University of Technology. Her current research interests include forward-looking imaging, compressed sensing, and sparse signal reconstruction. E-mail: jiexia@mail.ustc.edu.cn

Yin Zhiping (1980–) received his B.E. degree in electronic engineering and the Ph.D. degree in electromagnetic field and microwave technology from the University of Science and Technology of China (USTC), Hefei, China, in 2003 and 2008, respectively. From 2009 to 2010, he worked in the Microwave and Millimeter-wave Engineering Research Center, USTC, as a postdoctor. Now, he is an associate professor of the Academy of Photoelectric Technology, Hefei University of Technology, Hefei, China. His current research interests include microwave and terahertz device, phased-array antenna and microwave imaging radar. E-mail: zpyin@hfut.edu.cn

Chen Weidong (1968–) received his B.E. degree from University of Electronic Science and Technology of China, in 1990, and the M.E. and Ph.D. degrees both from University of Science and Technology of China (USTC), Hefei, China, in 1994 and 2005, respectively. Since 1994, he was been with the Department of Electronic Engineering and Information Science, USTC, where he is now a professor. His research interests include microwave imaging, microwave and millimeter wave technology and system, and radar imaging. E-mail: wdchen@ustc.edu.cn
Corresponding author: Chen Weidong E-mail: wdchen@ustc.edu.cn
Received Date: 2017-11-20
Rev Recd Date: 2018-05-09

Available Online: 2018-06-22

Publish Date: 2018-06-01

Abstract

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 ill-posed problem of deconvolution and the poor resolution of the Wiener filter. The performance of the proposed method is demonstrated by the real experimental data.
- High resolution radar imaging,
- Echo denosing,
- Two-dimensional deconvolution,
- Low rank matrix recovery

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References(17)

References

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沈阳化工大学材料科学与工程学院沈阳 110142

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

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