IEEE Geoscience and Remote Sensing Magazine - March 2022 - 202

significantly, resulting in reduced efficiency for MoCo/
autofocus processing. Therefore, fundamentally reducing
the amount of radar raw data to improve MoCo/autofocus
efficiency should be considered. Sparse imaging
mechanisms can break the requirement of the traditional
Nyquist sampling theorem and significantly reduce the
amount of radar raw data. The incorporation of sparse imaging
[100], [101] could improve the efficiency of MoCo/
autofocus processing.
OTHER TRENDS IN MOTION COMPENSATION/
AUTOFOCUS METHODS
WIDE-BEAM AUTOFOCUS TECHNOLOGY
Wide-beam Moco/autofocus technology mainly focuses
on how to compensate for motion errors, which largely
depends on the support of high-precision INS/GPS data.
Unfortunately, in many cases, the data cannot be obtained.
Therefore, the accurate estimation of motion errors in the
case of a wide beam is urgently needed, and wide-beam autofocus
is an urgent direction for future research.
HYBRID AUTOFOCUS TECHNOLOGY
This article pointed out the advantages and disadvantages
of standard autofocus methods. One can combine multiple
autofocus methods to overcome the disadvantages of
a single approach, which is a promising future development
direction.
REAL-TIME MOTION COMPENSATION/
AUTOFOCUS TECHNOLOGY
Real-time imaging methods for an ideal trajectory are mature,
but few of them can handle motion errors. The main
difficulty lies in how to estimate and correct motion errors
in real time. With the continuous development of realtime
requirements in the future, this area of MoCo/autofocus
may become an important development direction.
Additionally, deep learning algorithms may be promising
candidates for real-time MoCo/autofocus techniques.
Most of the time spent on autofocus processing coupled
with deep learning algorithms goes toward training.
Therefore, once training is complete, the combined use of
real-time MoCo/autofocus methods and deep learning algorithms
should be feasible.
ACKNOWLEDGMENTS
This study was supported, in part, by the National Natural
Science Foundation of China, under grant 61901531, and
the National Science Fund for Distinguished Young Scholars,
under grant 61825105.
AUTHOR INFORMATION
Jianlai Chen (jianlaichen@163.com) was born in Hengyang,
Hunan, China, in 1990. He received the B.S. degree
in electronic engineering and Ph.D. degree in signal and
information processing from Xidian University, Xi'an,
202
China, in 2013 and 2018, respectively. He is an associate
professor at Central South University, Changsha, 410083,
China. He has authored or coauthored one book and more
than 30 research articles in high-impact, peer-reviewed
journals. He serves as a guest editor for special issues of
Remote Sensing and Sensors. His research interests include
synthetic aperture radar (SAR) imaging, SAR motion compensation
and autofocus, and machine learning. He is a
Member of IEEE.
Mengdao Xing (xmd@xidian.edu.cn) received the
B.S. and Ph.D. degrees from Xidian University in 1997
and 2002, respectively. He is a professor at the National
Laboratory of Radar Signal Processing, Xidian University,
Xi'an, Shaanxi, 710071, China, where he is also the
dean of the Academy of Advanced Interdisciplinary Research.
His research interests include synthetic aperture
radar (SAR), SAR interferometry, inverse SAR, sparse
signal processing, and microwave remote sensing. He
has written or cowritten more than 200 refereed scientific
journal papers. He also has authored or coauthored
two books about SAR signal processing. His research has
been cited more than 10,000 times, with an h-index of
50, and he was rated a most-cited Chinese researcher by
Elsevier. He has been granted more than 50 patents in
China, and his research has been supported by various
funding programs, such as the National Science Fund for
Distinguished Young Scholars. He has led several special
issues in IEEE Geoscience and Remote Sensing Magazine and
IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing. He serves as an associate editor of radar
remote sensing for IEEE Transactions on Geoscience and
Remote Sensing and the editor-in-chief of Sensors. He is a
Fellow of IEEE.
Hanwen Yu (yuhanwenxd@gmail.com) was born in
Xi'an, Shaanxi, China, in 1985. He received the B.S. and
Ph.D. degrees in electronic engineering from Xidian
University, Xi'an, in 2007 and 2012, respectively. He
was a postdoctoral research fellow in the Department of
Civil and Environmental Engineering, National Center
for Airborne Laser Mapping, University of Houston. He
is currently a full professor in the School of Resources
and Environment, University of Electronic Science
and Technology of China, Chengdu, Sichuan, 611731,
China. He has authored or coauthored more than 35 research
articles in high-impact, peer-reviewed journals,
such as IEEE Transactions on Geoscience and Remote Sensing,
IEEE Transactions on Image Processing, and Remote
Sensing of Environment. His research interests include
phase unwrapping, machine learning, and interferometric
synthetic aperture radar (InSAR) signal processing
and applications. He has reviewed more than 240
manuscripts for 20-plus journals and was named an
IEEE Transactions on Geoscience and Remote Sensing best
reviewer in 2019. He serves as a topical associate editor
for IEEE Transactions on Geoscience and Remote Sensing,
and he is an associate editor of IEEE Geoscience and
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MARCH 2022
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