scholarly journals Deep learning for high‐resolution estimation of clutter angle‐Doppler spectrum in STAP

2021 ◽  
Author(s):  
Keqing Duan ◽  
Hui Chen ◽  
Wenchong Xie ◽  
Yongliang Wang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Doppler Spectrum ◽  
Resolution Estimation

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.

HQS-HRINet: An unrolled deep learning method for seismic high-resolution inversion with an inaccurate wavelet

2021 ◽  
Author(s):  
H. Chen ◽  
J.H. Gao ◽  
Z.Q. Gao ◽  
S.A. Shen ◽  
Z.Q. Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Learning Method
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