scholarly journals Elevation Spatial Variation Error Compensation in Complex Scene and Elevation Inversion by Autofocus Method in GEO SAR

2021 ◽  
Vol 13 (15) ◽  
pp. 2916
Author(s):  
Faguang Chang ◽  
Dexin Li ◽  
Zhen Dong ◽  
Yang Huang ◽  
Zhihua He
Keyword(s):  
Spatial Variation ◽  
Phase Error ◽  
Random Phase ◽  
Synthetic Aperture ◽  
Fast Variable ◽  
Complex Scene ◽  
Phase Gradient ◽  
Disaster Monitoring ◽  
Autofocus Algorithm ◽  
Target Elevation

Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such as in disaster monitoring and vegetation measurements. However, because of the elevation of the target, elevation spatial variation error is caused in the GEO SAR imaging. In this paper, we focus on the compensation of the elevation space-variant error in the fast variant part with the autofocus method and utilize the error to carry out elevation inversing in complex scenes. For a complex scene, it can be broken down into a slow variant slope and the remaining fast variant part. First, the phase error caused by the elevation spatial variation is analyzed. Second, the spatial variant error caused by the slowly variant slope is compensated with the improved imaging algorithm. The error caused by the remaining fast variable part is the focus of this paper. We propose a block map-drift phase gradient autofocus (block-MD-PGA) algorithm to compensate for the random phase error part. By dividing sub-blocks reasonably, the elevation spatial variant error is compensated for by an autofocus algorithm in each sub-block. Because the errors of different elevations are diverse, the proposed algorithm is suitable for the scene where the target elevations are almost the same after the sub-blocks are divided. Third, the phase error obtained by the autofocus method is used to inverse the target elevation. Finally, simulations with dot-matrix targets and targets based on the high-resolution TerraSAR-X image verify the excellent effect of the proposed method and the accuracy of the elevation inversion.

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.

Performance bounds of the phase gradient autofocus algorithm for synthetic aperture ladar

2009 ◽  
Author(s):  
Phillip Gatt ◽  
Don Jacob ◽  
Bert Bradford ◽  
Joe Marron ◽  
Brian Krause
Keyword(s):  
Synthetic Aperture ◽  
Performance Bounds ◽  
Phase Gradient ◽  
Autofocus Algorithm

Application and Improvement of Phase Gradient Autofocus Algorithm in Synthetic Aperture Lidar

2016 ◽  
Vol 53 (6) ◽  
pp. 062801
Author(s):  
张洁 Zhang Jie ◽  
王然 Wang Ran ◽  
张珂殊 Zhang Keshu
Keyword(s):  
Synthetic Aperture ◽  
Phase Gradient ◽  
Autofocus Algorithm

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.

A channel phase error compensation method for multi-channel synthetic aperture ladar

Optik ◽  
2019 ◽  
Vol 178 ◽  
pp. 830-840
Author(s):  
Shuai Wang ◽  
Maosheng Xiang ◽  
Bingnan Wang ◽  
Fubo Zhang ◽  
Yirong Wu
Keyword(s):  
Error Compensation ◽  
Phase Error ◽  
Compensation Method ◽  
Synthetic Aperture

scholarly journals Determination and correction of aberrations in full field OCT using phase gradient autofocus by maximizing the likelihood function

2020 ◽  
Author(s):  
Vasily Matkivsky ◽  
Alexander Moiseev ◽  
Pavel Shilyagin ◽  
Alexander Rodionov ◽  
Hendrik Spahr ◽  
...  
Keyword(s):  
High Frequency ◽  
Likelihood Function ◽  
Synthetic Aperture ◽  
Numerical Estimation ◽  
Optical Aberrations ◽  
Phase Gradient ◽  
Full Field ◽  
Fundus Imaging ◽  
Spatial Frequencies ◽  
Estimation And Correction

A method for numerical estimation and correction of aberrations of the eye in fundus imaging with optical coherence tomography (OCT) is presented. Aberrations are determined statistically by using the estimate based on likelihood function maximization. The method can be considered as an extension of the phase gradient autofocusing algorithm in synthetic aperture radar imaging to 2D optical aberrations correction. The efficiency of the proposed method has been demonstrated in OCT fundus imaging with 6λ aberrations. After correction, single photoreceptors were resolved. It is also shown that wavefront distortions with high spatial frequencies can be determined and corrected.Graphical Abstract for Table of Contents[Text. This work is dedicated to development a method for numerical estimation and correction of aberrations of the eye in fundus imaging with OCT. Aberration evaluation is performed statistically by using estimate based on likelihood function maximization. The efficiency of the proposed method has been demonstrated in OCT fundus imaging with 6λ aberrations. It has been shown that spatial high-frequency wavefront distortions can be determined]

scholarly journals Modified Chirp Scaling Algorithm for Ultra-High Resolution Spaceborne-SAR

2021 ◽  
Vol 2083 (3) ◽  
pp. 032048
Author(s):  
Tao He ◽  
Pengbo Wang ◽  
Jixiang Ma ◽  
Xinkai Zhou ◽  
Lingling Xue
Keyword(s):  
High Resolution ◽  
Phase Error ◽  
Equation Model ◽  
Synthetic Aperture ◽  
Signal Spectrum ◽  
Integration Time ◽  
Range Cell ◽  
Range Cell Migration ◽  
Range Equation ◽  
Chirp Scaling Algorithm

Abstract The hyperbolic range equation model (HREM) and equivalent squint range model (ESRM) are applied in traditional chirp scaling algorithm (CSA). However, these range models cannot describe the satellite range history in the high-resolution case accurately because of the long azimuth integration time. The non-negligible phase error caused by this will lead the targets distort. In this paper, a modified chirp scaling algorithm (MCSA) is proposed by introducing a novel high-precision range model. A more accurate signal spectrum is calculated through it. Then, the modified chirp scaling factor, range compression filter, range cell migration correction (RCMC) filter and azimuth compression filter can be derived based on this signal spectrum, and the focused target obtained at last. Finally, the experimental results, to validate the proposed algorithm, adopted by the sliding spotlight synthetic aperture radar (SAR) simulation are provided.

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