An Autofocus Back Projection Algorithm for GEO SAR Based on Minimum Entropy

Due to the extremely high orbital height and long synthetic aperture time, the geosynchronous synthetic aperture radar (GEO SAR) will inevitably suffer from different types of undesired errors, including atmosphere, orbital measurement error, antenna vibration, and scenery height fluctuation; moreov...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-14
Main Authors: Ding, Zegang, Zhu, Kaiwen, Zhang, Tianyi, Li, Linghao, Wang, Yan, Wang, Guanxing, Gao, Yongpeng, Wei, Yangkai, Zeng, Tao
Format: Article
Language:English
Subjects:
Algorithms
Antennas
Autofocus
back projection (BP)
Computer simulation
Digital Elevation Models
Entropy
Error analysis
Errors
Fluctuations
geosynchronous synthetic aperture radar (GEO SAR)
Height
Image degradation
Image processing
Image quality
Image segmentation
Imaging
minimum entropy
Orbits
Phase error
SAR (radar)
Synthetic aperture radar
Trajectory
Vibration
Vibration measurement
Vibrations
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Summary:Due to the extremely high orbital height and long synthetic aperture time, the geosynchronous synthetic aperture radar (GEO SAR) will inevitably suffer from different types of undesired errors, including atmosphere, orbital measurement error, antenna vibration, and scenery height fluctuation; moreover, because of the extremely large imaging swath, these undesired errors also have severe 2-D spatial variance. Thus, the autofocus processing plays a very important role in GEO SAR. However, current autofocus algorithms cannot handle all of the aforementioned complicated and 2-D spatial-variant errors simultaneously. In this article, an autofocus back projection (BP) method for GEO SAR based on minimum entropy is proposed. First, the BP algorithm based on a digital elevation model (DEM) is adopted to deal with the scenery height fluctuation. Then, the 2-D image segmentation is conducted to solve the spatial variance of the undesired errors. Subsequently, without the assumption of error type and considering both the amplitude error and phase error, the autofocus processing based on minimum entropy and adaptive moment estimation (Adam) is conducted to estimate the undesired errors iteratively and precisely. Moreover, the aperture division and sub-aperture fusion will also be utilized to alleviate the image quality degradation or even defocus, which could also improve the precision of error estimation. Finally, computer simulation results validate the effectiveness of the proposed method.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3164922