Weighted Minimum Entropy Autofocus Algorithm for ISAR Imaging

2011 ◽  
Vol 33 (8) ◽  
pp. 1809-1815
Author(s):  
Gang Xu ◽  
Lei Yang ◽  
Lei Zhang ◽  
Ya-chao Li ◽  
Meng-dao Xing
Keyword(s):  
Minimum Entropy ◽  
Isar Imaging ◽  
Autofocus Algorithm

Analysis on the Effect of Nonequivalent Relationship between Space and Time in MIMO-ISAR Imaging

2014 ◽  
Vol 35 (8) ◽  
pp. 1806-1812
Author(s):  
Gang Chen ◽  
Hong Gu ◽  
Wei-min Su ◽  
Hua Shao
Keyword(s):  
Space And Time ◽  
Isar Imaging

Preserving Unique Structural Blocks of Targets in ISAR Imaging by Pitman–Yor Process

2021 ◽  
Vol 21 (2) ◽  
pp. 1859-1876
Author(s):  
Di Cheng ◽  
Bo Yuan ◽  
Yulong Dai ◽  
Chang Chen ◽  
Weidong Chen
Keyword(s):  
Isar Imaging ◽  
Structural Blocks

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

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