Sparse aperture isar imaging method based on continuous compression sensing

2021 ◽  
Author(s):  
M. Lv ◽  
H. Chen ◽  
X. Zhao ◽  
L. Zhao ◽  
P. Xu
Keyword(s):  
Imaging Method ◽  
Isar Imaging ◽  
Sparse Aperture ◽  
Compression Sensing

An Efficient ISAR Imaging Method for Non-Uniformly Rotating Targets Based on Multiple Geometry-Aided Parameters Estimation

2019 ◽  
Vol 19 (6) ◽  
pp. 2191-2204
Author(s):  
Jun Zhang ◽  
Guisheng Liao ◽  
Shengqi Zhu ◽  
Jingwei Xu ◽  
Lihuan Huo ◽  
...  
Keyword(s):  
Parameters Estimation ◽  
Imaging Method ◽  
Isar Imaging

Sparse aperture ISAR imaging algorithm based on adaptive filtering framework

2019 ◽  
Vol 13 (3) ◽  
pp. 445-455 ◽  
Author(s):  
Zeng Chuangzhan ◽  
Zhu Weigang ◽  
Jia Xin
Keyword(s):  
Adaptive Filtering ◽  
Imaging Algorithm ◽  
Isar Imaging ◽  
Sparse Aperture

scholarly journals High-Resolution ISAR Imaging With Sparse Aperture VFM Waveforms Under Low SNR Condition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110651-110659
Author(s):  
Jiyuan Chen ◽  
Letao Xu ◽  
Xiaoyi Pan ◽  
Pu Zheng ◽  
Shunping Xiao
Keyword(s):  
High Resolution ◽  
Low Snr ◽  
Isar Imaging ◽  
Sparse Aperture

scholarly journals AN ISAR IMAGING METHOD FOR SEARCH RADAR INVOLVING NONUNIFORM ANGLE SAMPLES

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

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
Keyword(s):  
Imaging Method ◽  
Multiple Targets ◽  
Isar Imaging

scholarly journals CIST: An Improved ISAR Imaging Method Using Convolution Neural Network

2020 ◽  
Vol 12 (16) ◽  
pp. 2641
Author(s):  
Shunjun Wei ◽  
Jiadian Liang ◽  
Mou Wang ◽  
Xiangfeng Zeng ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Computational Cost ◽  
Convolution Neural Network ◽  
Imaging Method ◽  
Sampled Data ◽  
High Quality ◽  
Imaging Methods ◽  
Adaptive Parameter ◽  
Imaging Results ◽  
Isar Imaging

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.

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