The effect of polar format resampling on uncompensated motion phase errors and the phase gradient autofocus algorithm

2002 ◽  
Author(s):  
M. Preiss ◽  
D. Gray ◽  
N.J.S. Stacy
Keyword(s):  
Phase Gradient ◽  
Phase Errors ◽  
Autofocus Algorithm

scholarly journals Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2370
Author(s):  
Haemin Lee ◽  
Chang-Sik Jung ◽  
Ki-Wan Kim
Keyword(s):  
Phase Error ◽  
Minimum Entropy ◽  
Motion Error ◽  
Phase Gradient ◽  
Limited Bandwidth ◽  
Phase Errors ◽  
Feature Preserving ◽  
Autofocus Algorithm ◽  
Autofocus Algorithms ◽  
The Cost

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.

A Generalized Phase Gradient Autofocus Algorithm

2019 ◽  
Vol 5 (4) ◽  
pp. 606-619 ◽  
Author(s):  
Aaron Evers ◽  
Julie Ann Jackson
Keyword(s):  
Phase Gradient ◽  
Autofocus Algorithm
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