Joint image formation and two-dimensional autofocusing for synthetic aperture radar data

被引:10
作者
Scarnati, Theresa [1 ,2 ]
Gelb, Anne [3 ]
机构
[1] Arizona State Univ, Sch Math & Stat Sci, Tempe, AZ 85287 USA
[2] Air Force Res Lab, Wright Patterson AFB, OH USA
[3] Dartmouth Coll, Dept Math, Hanover, NH 03755 USA
基金
美国国家科学基金会;
关键词
Synthetic aperture radar; Autofocus; Phase error correction; High order sparsity regularization; Phase synchronization; PHASE GRADIENT AUTOFOCUS; FAST FOURIER-TRANSFORMS; SAR; RECONSTRUCTION; RESOLUTION; ALGORITHM; SPARSITY;
D O I
10.1016/j.jcp.2018.07.059
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Imaging via synthetic aperture radar (SAR) is a well-established technique for effective scene reconstruction, with resolution up to a few centimeters. The measurement process requires the round trip time for the electromagnetic waves to travel to the scene and return back to the sensing mechanism. While hypothetically the round trip time can be exactly determined, in practice this distance can only be approximated, leading to errors in the round trip time estimates. These errors manifest as phase errors on the data and produce defocused imagery, making information extraction difficult. This investigation develops an autofocusing technique that exploits the correlation of the phase error on both the azimuth angle and spatial (cycles/meter) frequencies while also enforcing the piecewise smooth nature of the image. Our method estimates the phase error correction and the image through a joint optimization procedure. Specifically, our method incorporates a phase synchronization technique to estimate the unknown two-dimensional phase error. High order regularization is used in the optimization procedure, which helps to reduce speckle in the SAR image. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:803 / 821
页数:19
相关论文
共 40 条
[1]  
[Anonymous], 1995, MSTAR OV
[2]  
[Anonymous], SPRINGER SCI
[3]  
[Anonymous], 2009, THESIS
[4]  
[Anonymous], 1996, Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
[5]   Polynomial fitting for edge detection in irregularly sampled signals and images [J].
Archibald, R ;
Gelb, A ;
Yoon, JH .
SIAM JOURNAL ON NUMERICAL ANALYSIS, 2005, 43 (01) :259-279
[6]   Image Reconstruction from Undersampled Fourier Data Using the Polynomial Annihilation Transform [J].
Archibald, Rick ;
Gelb, Anne ;
Platte, Rodrigo B. .
JOURNAL OF SCIENTIFIC COMPUTING, 2016, 67 (02) :432-452
[7]   An Autofocus Method for Backprojection Imagery in Synthetic Aperture Radar [J].
Ash, Joshua N. .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2012, 9 (01) :104-108
[8]   2-POINT STEP SIZE GRADIENT METHODS [J].
BARZILAI, J ;
BORWEIN, JM .
IMA JOURNAL OF NUMERICAL ANALYSIS, 1988, 8 (01) :141-148
[9]   A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [J].
Beck, Amir ;
Teboulle, Marc .
SIAM JOURNAL ON IMAGING SCIENCES, 2009, 2 (01) :183-202
[10]   PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming [J].
Candes, Emmanuel J. ;
Strohmer, Thomas ;
Voroninski, Vladislav .
COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 2013, 66 (08) :1241-1274