scholarly journals A fast two dimensional joint linearized bregman iteration algorithm for super-resolution inverse synthetic aperture radar imaging at low signal-to-noise ratios

2016 ◽  
Vol 65 (3) ◽  
pp. 038401
Author(s):  
Li Shao-Dong ◽  
Chen Wen-Feng ◽  
Yang Jun ◽  
Ma Xiao-Yan
Keyword(s):  
Synthetic Aperture Radar ◽  
Super Resolution ◽  
Radar Imaging ◽  
Synthetic Aperture ◽  
Iteration Algorithm ◽  
Two Dimensional ◽  
Inverse Synthetic Aperture Radar ◽  
Signal To Noise ◽  
Bregman Iteration ◽  
Linearized Bregman Iteration

scholarly journals Super resolution algorithm for satellites inverse synthetic aperture radar imaging

2018 ◽  
Vol 2 (2) ◽  
pp. 67-86 ◽  
Author(s):  
Alexandr A. Kazantsev ◽  
◽  
Denis A. Perov ◽  
Alexey A. Samorodov ◽  
Boris A. Samorodov ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Super Resolution ◽  
Radar Imaging ◽  
Synthetic Aperture ◽  
Inverse Synthetic Aperture Radar ◽  
Resolution Algorithm ◽  
Aperture Radar

scholarly journals Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profile Correlation

2020 ◽  
Author(s):  
Yue Lu ◽  
Jian Yang ◽  
Yue Zhang ◽  
Shiyou Xu
Keyword(s):  
Synthetic Aperture Radar ◽  
Signal To Noise Ratio ◽  
Radar Imaging ◽  
Synthetic Aperture ◽  
Minimum Entropy ◽  
Inverse Synthetic Aperture Radar ◽  
Signal To Noise ◽  
Range Alignment ◽  
Noise Ratio ◽  
Aperture Radar

Abstract Range alignment is an essential procedure in the translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum-correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novelanti-noise range alignment approach is proposed. In this new method, the target motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once the range profiles of eachsub-aperture are aligned, the non-coherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture because the former step focuses mainly on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.

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