scholarly journals AN EFFICIENT ISAR IMAGING METHOD FOR MULTIPLE TARGETS

2014 ◽  
Vol 146 ◽  
pp. 133-142 ◽  
Author(s):  
Joo-Ho Jung ◽  
Kyung-Tae Kim ◽  
Si-Ho Kim ◽  
Sang-Hong Park
2019 ◽  
Vol 19 (6) ◽  
pp. 2191-2204
Author(s):  
Jun Zhang ◽  
Guisheng Liao ◽  
Shengqi Zhu ◽  
Jingwei Xu ◽  
Lihuan Huo ◽  
...  

2018 ◽  
Vol 65 ◽  
pp. 9-17
Author(s):  
Hyung-Ju Kim ◽  
Kee Ung Bae ◽  
Won-Young Song ◽  
Eunjung Yang ◽  
Noh-Hoon Myung
Keyword(s):  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 199759-199767
Author(s):  
Mengdi Zhang ◽  
Guisheng Liao ◽  
Jingwei Xu ◽  
Lan Lan ◽  
Ka Deng

2020 ◽  
Vol 12 (16) ◽  
pp. 2641
Author(s):  
Shunjun Wei ◽  
Jiadian Liang ◽  
Mou Wang ◽  
Xiangfeng Zeng ◽  
Jun Shi ◽  
...  

Compressive sensing (CS) has been widely utilized in inverse synthetic aperture radar (ISAR) imaging, since ISAR measured data are generally non-completed in cross-range direction, and CS-based imaging methods can obtain high-quality imaging results using under-sampled data. However, the traditional CS-based methods need to pre-define parameters and sparse transforms, which are tough to be hand-crafted. Besides, these methods usually require heavy computational cost with large matrices operation. In this paper, inspired by the adaptive parameter learning and rapidly reconstruction of convolution neural network (CNN), a novel imaging method, called convolution iterative shrinkage-thresholding (CIST) network, is proposed for ISAR efficient sparse imaging. CIST is capable of learning optimal parameters and sparse transforms throughout the CNN training process, instead of being manually defined. Specifically, CIST replaces the linear sparse transform with non-linear convolution operations. This new transform and essential parameters are learnable end-to-end across the iterations, which increases the flexibility and robustness of CIST. When compared with the traditional state-of-the-art CS imaging methods, both simulation and experimental results demonstrate that the proposed CIST-based ISAR imaging method can obtain imaging results of high quality, while maintaining high computational efficiency. CIST-based ISAR imaging is tens of times faster than other methods.


2008 ◽  
Vol 22 (2-3) ◽  
pp. 365-373 ◽  
Author(s):  
S. H. Park ◽  
K. K. Park ◽  
J. H. Jung ◽  
H. T. Kim ◽  
K. T. Kim

2003 ◽  
Vol 86 (7) ◽  
pp. 1-10 ◽  
Author(s):  
Kazuhiko Yamamoto ◽  
Masafumi Iwamoto ◽  
Takahiko Fujisaka ◽  
Tetsuo Kirimoto

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