Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming
Linear Array Synthetic Aperture Radar (LASAR) is a novel and promising radar imaging technique. In recent years, Compressed Sensing (CS) sparse recovery has been a research focus for high-resolution three-Dimensional (3-D) LASAR imaging. Compared with the traditional two-Dimensional (2-D) SAR imagin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2018-12-01
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| Series: | Leida xuebao |
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| Online Access: | http://radars.ie.ac.cn/fileup/HTML/R17103.htm |
| _version_ | 1797427215869149184 |
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| author | Wei Shunjun Tian Bokun Zhang Xiaoling Shi Jun |
| author_facet | Wei Shunjun Tian Bokun Zhang Xiaoling Shi Jun |
| author_sort | Wei Shunjun |
| collection | DOAJ |
| description | Linear Array Synthetic Aperture Radar (LASAR) is a novel and promising radar imaging technique. In recent years, Compressed Sensing (CS) sparse recovery has been a research focus for high-resolution three-Dimensional (3-D) LASAR imaging. Compared with the traditional two-Dimensional (2-D) SAR imaging, LASAR suffers from many problems, including under-sampling data and multi-dimensional and higher-order phase errors due to its sparse Linear Array Antenna (LAA) and the joint 2-D motions of the platform and LAA. The conventional autofocusing methods of 2-D SAR may be not suitable for CS-based LASAR 3-D sparse autofocusing. To address the multi-dimensional and higher-order phase errors in LASAR 3-D imaging with respect to under-sampling data, in this paper, we propose a sparse autofocusing algorithm based on semidefinite programming for CS-based LASAR imaging. First, by combining CS-based imaging theory, image maximum sharpness, and the minimum square error principle, we construct a LASAR phase-error estimation model based on under-sampled data. Next, we use semi-definite programming relaxation to estimate the phase errors. Lastly, we employ an iterated approximation method to improve the precision of the phase-error estimation and achieve the final CS-based LASAR autofocusing. To further improve the efficiency of the algorithm, we select only the dominant scattering areas for LASAR phase-error estimation. We present our simulation and experimental results to confirm the effectiveness of out proposed algorithm. |
| first_indexed | 2024-03-09T08:41:05Z |
| format | Article |
| id | doaj.art-8d2faacf8849440da6fb126f6ba7efe7 |
| institution | Directory Open Access Journal |
| issn | 2095-283X 2095-283X |
| language | English |
| last_indexed | 2024-03-09T08:41:05Z |
| publishDate | 2018-12-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-8d2faacf8849440da6fb126f6ba7efe72023-12-02T16:47:39ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2095-283X2018-12-017666467510.12000/JR17103Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite ProgrammingWei Shunjun0Tian Bokun1Zhang Xiaoling2Shi Jun3(School of Electronic Engineering University of Electronic Science and Technology of China, Chengdu 611731, China)(School of Electronic Engineering University of Electronic Science and Technology of China, Chengdu 611731, China)(School of Electronic Engineering University of Electronic Science and Technology of China, Chengdu 611731, China)(School of Electronic Engineering University of Electronic Science and Technology of China, Chengdu 611731, China)Linear Array Synthetic Aperture Radar (LASAR) is a novel and promising radar imaging technique. In recent years, Compressed Sensing (CS) sparse recovery has been a research focus for high-resolution three-Dimensional (3-D) LASAR imaging. Compared with the traditional two-Dimensional (2-D) SAR imaging, LASAR suffers from many problems, including under-sampling data and multi-dimensional and higher-order phase errors due to its sparse Linear Array Antenna (LAA) and the joint 2-D motions of the platform and LAA. The conventional autofocusing methods of 2-D SAR may be not suitable for CS-based LASAR 3-D sparse autofocusing. To address the multi-dimensional and higher-order phase errors in LASAR 3-D imaging with respect to under-sampling data, in this paper, we propose a sparse autofocusing algorithm based on semidefinite programming for CS-based LASAR imaging. First, by combining CS-based imaging theory, image maximum sharpness, and the minimum square error principle, we construct a LASAR phase-error estimation model based on under-sampled data. Next, we use semi-definite programming relaxation to estimate the phase errors. Lastly, we employ an iterated approximation method to improve the precision of the phase-error estimation and achieve the final CS-based LASAR autofocusing. To further improve the efficiency of the algorithm, we select only the dominant scattering areas for LASAR phase-error estimation. We present our simulation and experimental results to confirm the effectiveness of out proposed algorithm.http://radars.ie.ac.cn/fileup/HTML/R17103.htmLinear Array Synthetic Aperture Radar (LASAR)Sparse autofocus imagingMaximum sharpnessSemi-Definite Programming (SDP)Compressed Sensing (CS) |
| spellingShingle | Wei Shunjun Tian Bokun Zhang Xiaoling Shi Jun Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming Leida xuebao Linear Array Synthetic Aperture Radar (LASAR) Sparse autofocus imaging Maximum sharpness Semi-Definite Programming (SDP) Compressed Sensing (CS) |
| title | Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming |
| title_full | Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming |
| title_fullStr | Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming |
| title_full_unstemmed | Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming |
| title_short | Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming |
| title_sort | compressed sensing linear array sar autofocusing imaging via semi definite programming |
| topic | Linear Array Synthetic Aperture Radar (LASAR) Sparse autofocus imaging Maximum sharpness Semi-Definite Programming (SDP) Compressed Sensing (CS) |
| url | http://radars.ie.ac.cn/fileup/HTML/R17103.htm |
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