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| Citation: | Wei Shunjun, Tian Bokun, Zhang Xiaoling, Shi Jun. Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming[J]. Journal of Radars, 2018, 7(6): 664-675. doi: 10.12000/JR17103
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Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming
DOI: 10.12000/JR17103
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Abstract
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 semi-definite 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. -
References
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LI Chunsheng, YU Ze, and CHEN Jie. Overview of techniques for improving high-resolution spaceborne SAR imaging and image quality[J]. Journal of Radars, 2019, 8(6): 717–731. doi: 10.12000/JR19085
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- Figure 1. The geographic model of down-looking LASAR imaging
- Figure 3. The results of the point targets in the case of the along-track random phase errors and cross-track random phase errors(Top: all samples; Middle: 50% samples; Bottom: 25% samples)
- Figure 4. The MSE curve of SDPSA
- Figure 2. The results of the point targets in the case of the along-track quadratic phase errors and cross-track random phase errors(Top: all samples; Middle: 50% samples; Bottom: 25% samples)
- Figure 5. The ground-based LASAR experiment
- Figure 6. The virtual array antenna and the echo of the ground-based LSAR
- Figure 7.
- Figure 8. The sparse autofocusing results of the experimental light data(Left: BP-PGA; Middle: IRLS-PGA; Right: SDPSA)