IEEE Geoscience and Remote Sensing Magazine - December 2022 - 61

the development of cognitive radar indicates that knowledge-aided
fully adaptive approaches that infer properties
of the environment with intelligent sensing can optimize
the radar performance and make full use of finite system
resources. The knowledge-aided technology can be transferred
to sparse SAR imaging with intelligent features, such
as optimal waveform design and anti-interference.
ACKNOWLEDGMENT
We would like to thank the editors and anonymous reviewers
for their valuable comments, which have helped to improve
the quality of this article. We would also like to thank Prof.
S. J. Wei and his group (University of Electronic Science and
Technology of China) for supporting the results of the RMIST
algorithm proposed in [205]. This work was supported in
part by the National Natural Science Foundation of China
under Grant 62071113, in part by the Natural Science Foundation
of Jiangsu Province under Grant BK20211559, and in
part by the Zhishan Scholar Program of Southeast University.
AUTHOR INFORMATION
Gang Xu (gangxu@seu.edu.cn) received his B.S. and Ph.D.
degrees in electrical engineering from Xidian University,
Xi'an China, in 2009 and 2015, respectively. From 2015 to
2016, he was a full-time postdoctoral research fellow with
the School of Electrical and Electronic Engineering, Nanyang
Technological University, Singapore. He is currently an associate
professor with the State Key Laboratory of Millimeter
Waves, School of Information Science and Engineering,
Southeast University, Nanjing 210096, China. His major
research interests include synthetic aperture radar (SAR)/
inverse SAR imaging, interferometric SAR processing, and
sparse radar imaging. He has authored and coauthored more
than 50 scientific articles and more than 20 papers in conference
proceedings. He has guest edited several special issues
of Remote Sensing and Frontiers in Signal Processing. He is a
Member of IEEE.
Bangjie Zhang (bjzhang1127@seu.edu.cn) received his
B.S. degree in information engineering from Southeast University,
Nanjing, China, in 2018. He is currently pursuing
his Ph.D. degree with the State Key Laboratory of Millimeter
Waves, School of Information Science and Engineering,
Southeast University, Nanjing 210096, China. His research
interests include millimeter-wave radar systems, synthetic aperture
radar (SAR) tomography, and inverse SAR imaging. He
is a Student Member of IEEE.
Hanwen Yu (yuhanwenxd@gmail.com) received his B.S.
and Ph.D. degrees from Xidian University, Xi'an, China, in
2007 and 2012, respectively. He was a postdoctoral fellow
with the Department of Civil and Environmental Engineering
and the National Center for Airborne Laser Mapping at
the University of Houston. He is a currently full professor
at the School of Resources and Environment, University of
Electronic Science and Technology of China, Chengdu, Sichuan
611731, China. He is an adjunct full professor at the
Academy of Advanced Interdisciplinary Research, Xidian
DECEMBER 2022 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
University, China, and is an adjunct full professor in the Department
of Engineering, University of Napoli Parthenope,
Italy. He has been deeply involved in IEEE (in general) and
the IEEE Geoscience and Remote Sensing Society (GRSS) in
particular. His research focuses on interferometric synthetic
aperture radar (InSAR), and this work has led to new insights
into worldwide deformation monitoring and topographic
mapping. He has authored more than 60 scientific papers
and articles and given a scientific presentation, " Advanced
Techniques in InSAR Phase Unwrapping, " invited by the
IEEE GRSS Webinar in 2021. He has reviewed more than 280
manuscripts for more than 20 different journals, and he was
elected as the 2019 IEEE Transactions on Geoscience and Remote
Sensing Best Reviewer. He was also elected as an IEEE GRSS
AdCom member beginning in 2023. Currently, he serves as
the editor-in-chief of IEEE Journal on Miniaturization for Air
and Space Systems, a topical associate editor of IEEE Transactions
on Geoscience and Remote Sensing, and an associate editor
of IEEE Geoscience and Remote Sensing Magazine. He is the
recipient of several awards and honors from the IEEE GRSS,
including the 2021 Transactions Prize Paper Award; the technical
program committee member and session chair of 2022
IGARSS; and the coprimary investigator of the IEEE GRSS
50%-funded project " West China Initiative. " He is a Senior
Member of IEEE.
Jianlai Chen (jianlaichen@163.com) received his B.S. degree
in electronic engineering and his Ph.D. degree in signal
and information processing from Xidian University, Xi'an,
China, in 2013 and 2018, respectively. He is currently an associate
professor with 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 has served as the guest editor for special
issues of Remote Sensing and Molecular Diversity Preservation
International 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 his B.S.
and Ph.D. degrees in electrical engineering from Xidian
University, Xi'an China, in 1997 and 2002, respectively. He
is currently a full professor with the National Laboratory of
Radar Signal Processing, Xidian University, Xi'an 410083,
China. He holds the appointment of dean at the Academy
of Advanced Interdisciplinary Research, Xidian University.
He has written or cowritten more than 200 refereed scientific
journal articles. He also has authored or coauthored two
books about synthetic aperture radar (SAR) signal processing.
The total number of citations of his research is greater
than 10,000 (an H-index of 50). He was rated as a Most-Cited
Chinese Researcher by Elsevier. He has achieved more than
50 authorized Chinese patents. His research has been supported
by various funding programs, such as the National
Science Fund for Distinguished Young Scholars. His research
interests include SAR, interferometric SAR, inverse SAR,
sparse signal processing, and microwave remote sensing. He
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