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Wide angle SAR imaging method based on Hybrid Representation
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  • Yao Zhao,
  • Yanxu Chen,
  • He Tian,
  • Xiangyin Quan,
  • Bingo Ling,
  • Zhe Zhang
Yao Zhao
Guangdong University of Technology
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Yanxu Chen
Guangdong University of Technology
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He Tian
Science and Technology on Electromagnetic Scattering Laboratory
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Xiangyin Quan
China Academy of Launch Vehicle Technology
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Bingo Ling
Guangdong University of Technology
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Zhe Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences

Corresponding Author:[email protected]

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In this paper, we investigate the application of Hybrid Representation in Wide-Angle Synthetic Aperture Radar (WASAR) imaging, addressing the challenges of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. We utilize a Convolutional Neural Network (CNN) to represent two-dimensional data within the same subaperture, while employing dictionary learning for sparse representation across different subapertures. Convolutional Neural Networks (CNNs) excel at learning spatial hierarchies and local dependencies in two-dimensional data, but require a large amount of training data. Isotropic targets within subapertures can be used for training with conventional SAR data, whereas anisotropic targets present challenges in obtaining training samples. To address this, a dictionary for different subapertures is generated from measurements using dictionary learning, eliminating the need for additional training data. By integrating these methods, we propose a novel approach, Hybrid-WASAR, which incorporates two regularization terms into WASAR imaging and employs the Alternating Direction Method of Multipliers (ADMM) to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, Hybrid-WASAR not only enhances the accuracy of the reconstructed target backscatter coefficients, but also effectively reduces sidelobes and noise, resulting in a significant improvement in overall imaging quality.
15 May 2023Submitted to Electronics Letters
15 May 2023Submission Checks Completed
15 May 2023Assigned to Editor
18 May 2023Reviewer(s) Assigned
01 Jun 2023Review(s) Completed, Editorial Evaluation Pending
02 Jun 2023Editorial Decision: Revise Major
30 Jun 20231st Revision Received
01 Jul 2023Assigned to Editor
01 Jul 2023Submission Checks Completed
01 Jul 2023Review(s) Completed, Editorial Evaluation Pending
04 Jul 2023Editorial Decision: Revise Minor
15 Jul 20232nd Revision Received
15 Jul 2023Submission Checks Completed
15 Jul 2023Assigned to Editor
15 Jul 2023Review(s) Completed, Editorial Evaluation Pending
17 Jul 2023Editorial Decision: Accept