Invited Speakers  特邀嘉宾


Prof. Haiquan Zhao
Southwest Jiaotong University, China


Haiquan Zhao (Senior Member, IEEE) was born in Henan Province, China, in 1974. He received the B.S. degree in applied mathematics, and the M.S. and Ph.D. degrees in signal and information processing from Southwest Jiaotong University, Chengdu, China, in 1998, 2005, and 2011, respectively. Since 2012, he has been a Professor with the School of Electrical Engineering, Southwest Jiaotong University. From 2015 to 2016, as a Visiting Scholar, he worked with the University of Florida, USA. His current research interests include nonlinear active noise control, information theoretical learning, neural networks, adaptive network, adaptive filtering algorithm, and power system frequency estimation. At present, he is the author or coauthor of more than 160 international journal papers (SCI indexed), and the owner of 49 granted invention patents. He has served as an Active Reviewer for several IEEE transactions, IET series, signal processing, and other international journals. At present, he is a Handling Editor of Signal Processing. Prof. Zhao has won several provincial and ministerial awards, and many best paper awards of international conferences and IEEE Transactions.

Speech Title: Robust Kalman Filter-based Information Theoretic Learning and Its Applications
Abstract: The unscented Kalman filter (UKF) is one of the important methods to implement power system state estimation. Its purpose is to correct the errors caused by phasor measurement unit (PMU) and other monitoring systems in power system, so as to obtain more accurate state estimation results. However, the performance of traditional UKF will degrade or even diverge in the non-Gaussian system where the measured values include impulse noises, Laplacian noises or outliers. In order to solve this problem, robust new UKFs based on the information theoretic learning (ITL) rule are proposed for state estimation of non-Gaussian systems.



Prof. Serkan Kiranyaz
Qatar University, Qatar


Serkan Kiranyaz was born in Turkey, 1972. He received his BS and MS degrees in Electrical and Electronics Department at Bilkent University, Ankara, Turkey, in 1994 and 1996, respectively. He received his PhD degree in 2005 and his Docency at 2007 from Tampere University of Technology, Institute of Signal Processing respectively. He was working as a Professor in Signal Processing Department in the same university during 2009 to 2015. He currently works as a Professor in Qatar University, Doha, Qatar.

Prof. Kiranyaz has noteworthy expertise and background in various signal processing domains. He published two books, 7 book chapters, 10 patents/applications, more than 100 journal articles in several IEEE Transactions and other high impact journals, and more than 120 papers in international conferences. He served as PI and LPI in several national and international projects. His principal research field is machine learning and signal processing. He is rigorously aiming for reinventing the ways in novel signal processing paradigms, enriching it with new approaches especially in machine intelligence, and revolutionizing the means of “learn-to-process” signals. He made significant contributions on bio-signal analysis, particularly EEG and ECG analysis and processing, classification and segmentation, computer vision with applications to recognition, classification, multimedia retrieval, evolving systems and evolutionary machine learning, swarm intelligence and evolutionary optimization.



Prof. Jibin Zheng
Xidian University, China


Jibin Zheng, Huashan Distinguished Professor, Doctoral supervisor, National Laboratory of Radar Signal Processing, Xidian University. He studied in Duke University from 2012 to 2014, and received the PhD degree from Xidian University in 2015. His main research directions include Radar detection and tracking of highly dynamic targets with complex electromagnetic scattering characteristics, Electron reconnaissance in complex electromagnetic spectrum environment, Cluster bat bionic radar. He has published 65 high-level papers in his research field, among which 9 papers have been awarded ESI highly cited papers, Natural Science Academic Paper Award of Shaanxi Province, etc. The research results have been applied in 6 types of radar equipment. He has presided over Basic Strengthening Project, National Natural Science Foundation, National Defense Basic Scientific Research Programs, etc.

Speech Title: Theory and Method of Autonomous Cooperative Detection via Cluster UAVs Equipped with Active and Passive Radars in Denial Environments
Abstract: Owing to the specific characteristics of Cluster Unmanned Aerial Vehicles (UAVs), the applications of Cluster UAVs increase dramatically both in civilian and military. However, the Global Positioning System (GPS) and the communications between UAVs may lose effectiveness or become unavailable due to the interference in the operational environment. This report will include the source localization in intertial navigation system (INS) in the GPS-denied area, the phased array radar detection, and the mission planning in communication denial environment, and discuss correaponding theory and method.



Prof. Shuwen Xu
Xidian University, China


Shuwen Xu (IEEE Senior Member) was born in Huangshan city in Anhui, China. He received the B.Eng. and Ph.D. degrees, both in electronic engineering, from Xidian University, Xi’an, China, in 2006 and 2011, respectively. His research interests are in the fields of radar target detection, statistical Learning, and SAR image processing. He worked at the National Laboratory of Radar Signal Processing, Xidian University, after that. He worked as a visiting professor in Mcmaster University in 2017 and 2018, Canada. He is currently a professor with the National Laboratory of Radar Signal Processing, Xidian University. He is also the vice director of National Collaborative Innovation Center of Information Sensing and Understanding and the Director of radar signal processing and data processing Department. He served as the editorial board member of "Journal of Electronics and Informatics", "Journal of Radar", " Signal Processing", " Journal of Terahertz Science and Electronic Information". And he has published and hired more than 100 academic papers, and authorized 20 patents.

Speech Title: Recent Progress of Multi-domain and Multi-dimensional Detection Methods for Small Sea Surface Targets
Abstract: Radar target detection in sea clutter is of significance to both civilian and military. With the miniaturization and invisibility of sea targets, floating small targets with slow speed have become the focus of radar detection. However, the detection of floating small targets in the background of sea clutter has always been a problem. Floating small targets usually have a weak Radar Cross Section (RCS) and slow speed, making it difficult to detect such targets in sea clutter. Traditional target detection methods exhibit poor performance in the detection of floating small targets. For the detection of small and weak targets on the sea surface, a high-Doppler-resolution and high-range-resolution system (double high system) is an effective way to solve this problem. In the double high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry and a subject of constant innovation. In recent years, under the double high system, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed a variety of featurebased target detection methods to alleviate the difficulty in detecting floating small targets when relying only on energy information and considerably improve detection performance. To ensure that relevant radar practitioners better understand the development and future trend of this field in recent years, this report summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.




Assoc. Prof. Li Zhang
University of Leeds, UK


Li Zhang is an Associate Professor and leads the Wireless Communication Group at the school of Electronic and Electrical Engineering, University of Leeds, UK. Her research interest is focused on wireless communications and signal processing techniques, such as massive MIMO, mmWave communications, Heterogeneous Network, Device to Device communications and 5G/5G beyond systems etc. She has served on the Technical Programme Committees of most major IEEE conferences in communications since 2006 and is an associate editor of IEEE journal. She has been selected as a member of the prestigious UK EPSRC Peer Review College since 2006, and regularly helps reviewing grant applications for Research councils in a number of European countries and book proposals for different publishers. She has been PhD examiner for numerous Universities. In 2005, she received a Nuffield award for a newly appointed lecturer. In 2006, she became a fellow of Higher Education Academy. In 2011, she was awarded as IEEE exemplary reviewer and in 2012 she was promoted as senior IEEE member.

Speech Title: Hybrid Precoding for mmWave Massive MIMO
Abstract: With the rapid growth of mobile users and various applications requiring high-speed wireless connections, the ever-increasing demand of high data rates cannot be fulfilled by the limited spectrum resource. With a large amount of wireless transmission bandwidth available at mmWave frequency bands, MmWave communications is considered as one of the key technologies in 5G and B5G networks to provide very high data rates. Short wavelength in such bands allows massive number of antennas to be packed within a small physical size area, and naturally massive MIMO is applied to offer beamforming gain required to overcome the high propagation loss in mmWave frequency bands. The precoding for mmWave massive MIMO system has attracted considerable attention. But there are still issues such as computational complexity, energy consumption and cost etc. This talk will discuss the hybrid precoding we proposed for mmWave massive MIMO system with the aim to mitigate these problems.




Assoc. Prof. Fei Wang
Nanjing University of Aeronautics and Astronautics, China


He received the B. S. degree in power engineering from HoHai University in 1998, and the M.S. and the Ph.D. degrees in signal processing from Jilin University in 2003 and 2006, respectively; in 2011, he worked as a visiting scholar at Queens University of Belfast for one year funded by the China Scholarship Council; in 2017, he was invited by the 14th IBCAST International Conference and the CIE radar 2021 to give special lectures; he has successively presided over or undertaken Aeronautical Science Foundation of China, National Natural Science Foundation of China, National Defense Pre-search Program and National Defense Program (973 Program); he has won the first prize of Jilin Province Science and Technology Progress Award twice, and the second prize of National Defense Science and Technology Progress Award twice; he has published 20 papers included in SCI, authorized 8 national invention patents, 2 software copyrights, and co-published 5 monographs.

Speech Title: Integrated Waveform Design of Radar and Communication Systems for the Single Transmitter Multiple Receivers System
Abstract: To realize the radar and communication integration of the Single Transmitter Multiple Receivers (STMR) system, a complete integrated waveform design method is proposed. Firstly, the necessary source information for successfully cooperative detection is designed and indispensable preprocessing is applied. Secondly, the integrated waveform called the DSSS-FH-MSK signal based on the spread spectrum technique and MSK modulation is designed to enable the signal to have the anti-interference and anti-intercept ability. The coded sequence goes through the Direct Sequence Spread Spectrum (DSSS) and is modulated by Minimum Shift Keying (MSK), then carrier frequency hops randomly by using the Hopping Frequency (HF) technique. Simulation results indicate that the DSSS-FH-MSK signal has a thumbtack type cross ambiguity function which means it has good estimation performance in the STMR system. Additionally, Low Probability of Intercept (LPI) evaluation methods are employed to verify that the designed signal has excellent LPI performance.




Assoc. Prof. Shuanghui Zhang
National University of Defense Technology, China

Shuanghui Zhang received the B.S. and Ph.D. degrees from National University of Defense Technology in 2011 and 2016 respectively, and he worked at Nanyang Technological University as a visiting Ph.D. student in 2015. He is currently an associate professor with the college of electrical science and technology, NUDT. His research interests lie in radar imaging and target recognition techniques, such as sparse aperture ISAR imaging, bistatic ISAR imaging, interferometric ISAR imaging, etc. He has published more than 40 articles in refereed journals, such as IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing, IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Aerospace and Electronic Systems, etc. He awarded the young elite scientists sponsorship by China Association for Science and Technology, and distinguished young scholars of Hunan Province. He won the excellent doctoral theses of Chinese Institute of Electronics.

Speech Title: Radar Imaging and Feature Extraction of Space Targets
Abstract: With the increase of space targets, the wide pulse observation resources are becoming more scarcer, and the demand for inverse synthetic aperture radar (ISAR) imaging is becoming increasingly prominent. The moving antenna and other micro-motion parts of space targets affect the quality of ISAR images, causing images defocusing, which is not conducive to extracting the feature information of the target. This report will discuss the ISAR imaging and feature extraction methods of space targets to alleviate the above problems. Firstly, the sparse imaging technology of space targets is introduced, including sparse Bayesian framework, deep unfolding network and imaging technology of space targets with micro-motion parts. Then, how to obtain the attitude of space targets according to ISAR images and target prior information is discussed. Finally, the existing research ideas of 3D imaging methods for space targets are summarized.




Assoc. Prof. Chenguang Shi
Nanjing University of Aeronautics and Astronautics, China

Chenguang Shi received the B.S. and Ph.D. degrees from the Nanjing University of Aeronautics and Astronautics (NUAA) in 2012 and 2017, respectively. He is currently an associate professor with the Key Laboratory of Radar Imaging and Microwave Photonics, NUAA, Ministry of Education. His current research interests include radar signal processing, low probability of intercept optimization, radar network, adaptive radar waveform design, and target tracking. He has published over 40 high-level journal papers and 30 refereed conference papers. He is currently engaged in over 10 research projects, which are supported by the National Natural Science Foundation of China, the National Defense Science and Technology Innovation Special Zones, the Key Laboratory of Equipment Pre-Research Foundation, and the National Aerospace Science Foundation of China. He has served as a chair for various technical tracks of many international conferences, including IEEE RadarConf, ICAUS, ICCAIS, CIE RADAR, ICGNC, ICTCE, etc. He has also served as a guest editor for the special issue “Radar Signal Processing for Resource Aware Management” for the EURASIP Journal on Advances in Signal Processing. He is a young editor of the Journal of Electronics and Information Technology, Aerospace Technology, Command Control and Simulation, and Unmanned Systems Technology. He is the recipient of the National Defense Science and Technology Progress Award, the Best Paper Award in ICAUS, the Best Paper Honorable Mentions Award in ICCAIS, and the Excellent Doctoral Dissertation Nomination Award from China Electronic Education Association.

Speech Title: Collaborative Transmit Resource Scheduling for Target Tracking in Multiple Radar Systems Based on Bi-Objective Optimization
Abstract: In this study, a bi-objective optimization-based collaborative transmit resource scheduling (BOO-CTRS) strategy is developed for target tracking in multiple radars system. The main aim of the proposed strategy is to improve the low probability of intercept (LPI) performance and target tracking accuracy of the underlying system while satisfying the resource budgets and waveform library limitation, where the probability of intercept and Bayesian Cramer-Rao Lower Bound (BCRLB) are utilized to evaluate the LPI performance and target tracking accuracy, respectively. It is shown that the resulting problem is a complex bi-objective and NP-hard optimization problem, where the four involved parameters, i.e., the transmit power, dwell time, waveform bandwidth, and pulse length, are all coupled in the objective functions and constraints. Combined with the particle swarm optimization (PSO) algorithm, we propose an efficient and fast three-step solution technique to solve the resulting optimization model, which fully meets the real-time requirement. Simulation results demonstrate the effectiveness and superiority of our proposed optimization strategy compared with other existing algorithms.




Asst. Prof. Yuchou Chang
University of Massachusetts Dartmouth, USA

Dr. Yuchou Chang is an assistant professor at the Computer and Information Science Department of University of Massachusetts Dartmouth. He received a PhD from the University of Wisconsin-Milwaukee and postdoctoral training from Barrow Neurological Institute of St. Joseph’s Hospital & Medical Center at Phoenix, Arizona. His current research interests include Biomedical Signal Processing, Magnetic Resonance Imaging, Robotics, and Brain-Computer Interface. He has authored and co-authored over 100 peer-reviewed publications. He has served as an associate editor of BMC Medical Imaging and multiple sessions’ chair/co-chair of the flagship conferences like IEEE EMBC and IEEE ICRA. He received E. Kika De La Garza Fellowship from the United States Department of Agriculture (USDA) in 2016. He has received several awards such as the Best Technical Report & Semi-Finalist of National Aeronautics and Space Administration (NASA) Swarmathon Robotics Virtual Competition 2017.

Speech Title: Noninvasive Electroencephalography Signal Analysis for Brain-Robot Interaction
Abstract: Electroencephalography (EEG) is a type of neurophysiological signals widely used for clinical applications such as diagnosis of brain disorders. Noninvasive EEG-based brain-computer interface (BCI) has been studied for non-medical uses of healthy human subjects. EEG-based BCI provides an emerging communication way between human and robot, and may enable intuitive, natural, and smooth interactions, as human-human interaction does. However, EEG signal is generally nonstationary. It is difficult to characterize EEG signal distributions for constantly making brain-robot interactions in a stable mode. To solve the nonstationary problem, both conventional signal processing and machine learning approaches will be introduced. Integrating both approaches can further improve signal nonstationary effect, and therefore enhance performance of brain-robot interaction.




Dr. Li You
Southeast University, China

Li You received the B.E. and M.E. degrees from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2009 and 2012, respectively, and the Ph.D. degree from Southeast University, Nanjing, in 2016, all in electrical engineering. From 2014 to 2015, he conducted Visiting Research at the Center for Pervasive Communications and Computing, University of California Irvine, Irvine, CA, USA. Since 2016, he has been with the Faculty of the National Mobile Communications Research Laboratory, Southeast University. His research interests lie in the general areas of communications, signal processing, and information theory, with the current emphasis on massive MIMO communications. Dr. You received the National Excellent Doctoral Dissertation Award from the China Institute of Communications (CIC) in 2017, the Young Elite Scientists Sponsorship Program (2019–2021) by the China Association for Science and Technology (CAST), and the URSI Young Scientist Award in 2021.

Speech Title: Massive MIMO Satellite Communications
Abstract: Massive MIMO transmission technique can make full use of the spatial degrees of freedom, thus greatly enhancing the spectral efficiency and energy efficiency, which is one of the most potential directions for supporting future space-air-ground integrated broadband green mobile communication systems. Though widely used in terrestrial communication systems, Massive MIMO techniques have not been applied to satellite communication systems. The main contents of our presentation include satellite massive MIMO channel characteristics analysis and statistical modeling, channel state information acquisition, downlink precoding, and user scheduling.




Asst. Prof. Ke Xu
Shanghai Jiao Tong University, China

Ke Xu received a Ph.D. degree from Shanghai Jiao Tong University, Shanghai, China, in 2019. He is currently an Assistant Professor with the Institute of Cyber Space Security, the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University. His research interests include gait recognition, action recognition, video adversarial attack, and abnormal events detection. He has been funded by a National Natural Science Foundation youth fund and a postdoctoral fund. He has published more than 30 scientific articles in refereed journals and conferences, such as IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, International Conference on Multimedia and Expo, etc. He is a member of CSIG digital media forensics and Security Professional Committee, a member of IEEE, and a member of CCF.

Speech Title: Video Gait Recognition based on Human Skeleton Model
Abstract: Gait is one of the biometric features for human identity recognition. Compared to other biometric features such as the face, iris, or fingerprint, gait is suitable for long-distance recognition. Gait recognition aims to identify a human through a walking sequence in a video. It is a challenging task in computer vision since the monocular camera loses most of the 3D information. This report describes the relevant research based on the human skeleton model in recent years.




Dr. Yanhong Xu
Xi'an University of Science and Technology, China

Yanhong Xu was born in Shandong Province, China, in 1989. She received the B.S. degree in electronic engineering and the Ph.D. degree in electromagnetic field and microwave technology from Xidian University, Xi’an, China, in 2012, and 2017, respectively. From 2018/01 to 2019/12, she was a Lecturer with the School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, China. Since 2019/12, she was elected as an Associate Professor in the same institute. During a period from 2018/02 to 2020/02, she is a Postdoctoral Fellow in the State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong. Her research interests include array antenna theory, frequency diverse array, wideband antenna and millimeter wave antenna technology. She was elected as an expert of “Thousand Talents Plan” (Young) of Shaanxi Province in 2020.

Speech Title: Range-Angle Transmit and/or Receive Beamforming of Frequency Diverse Array
Abstract: Array beamforming is a powerful technique for enhancing the performance of array antennas, with extensively applications in radar, sonar, acoustics, astronomy, seismology, communications, and medical imaging. However, for conventional phased array, its beam steering vector is fixed in one direction and its corresponding beamforming can only be performed in angle domain. A more flexible beamforming array, termed as frequency diverse array (FDA), has aroused increasingly attention of researchers. The FDA exploits a small frequency offset across the array elements, resulting in a range-angle-time-dependent beampattern. This is distinctly different from conventional phased array whose beampattern is independent of range in far field and boosts the flexibility of array beamforming.




Dr. Weiping Li
Xi’an Institute of Space Radio Technology, China

Weiping Li was born in JiangXi province, China. She received the B.S. degree in biomedical engineering and the Ph.D. degree in signal and information processing from Xidian University, Xi’an, China, in 2005, and 2013, respectively. Since 2013, she serves in Xi’an Institute of Space Radio Technology where she is promoted as Senior Research Fellow. Her research interests include spaceborne radar system design, multi-sensor array signal processing, synthetic aperture radar technique, and waveform design.

Speech Title: Research on the Space Debris Target Detection in Space-based TDM-MIMO Radar System
Abstract: For the detection of "sub centimeter" space debris, the space-based radar system is a better choice. Under the limited space borne antenna size, the TDM-MIMO radar system can greatly improve the angle resolution and angle measurement accuracy through the virtual aperture expansion. However, because the relative speed between the space debris and the radar is extremely large (even more than 10km/s), the variation of the range between the receiving array of radar and the target cannot be ignored. The equivalent virtual array is no longer a linear array, but a stepped array in space, which results in the energy defocusing. To address the issue, a correction and compensation method for spatial nonuniform array is proposed. Firstly, the motion characteristics of space debris are analyzed, and then the corresponding motion envelope correction method is used to realize array compensation correction. The complex distributed virtual array is compensated into a standard linear array, and then beam-forming and subsequent target detection and angle measurement are performed. Theoretical analysis and simulation results show that the proposed method is effective and valuable.




Dr. Himal Suraweera
University of Peradeniya, Sri Lanka

Himal A. Suraweera (Senior Member, IEEE) is a senior lecturer in the Department of Electrical and, Electronic Engineering, University of Peradeniya, Sri Lanka. He is in the editorial boards of IEEE Transactions on Communications and IEEE Open Journal of the Communications Society. Previously he has served as an editor for IEEE Communications Letters, IEEE Transactions on Wireless Communications and IEEE Transactions on Green Communications and Networking. He was a recipient/co-recipient of the IEEE ComSoC AP Outstanding Young Researcher Award in 2011 and the IEEE ComSoc Leonard G. Abraham Prize in 2017. He has been involved as a co-chair, Signal Processing for Communications Symposium of IEEE GLOBECOM 2015, and track chair of Full-Duplex Communications Track, Symposium on Selected Areas in Communications of IEEE ICC 2022. His research interests are in the areas of wireless communications, signal processing for communications and communication theory, in particular, cooperative communications systems, full-duplex wireless techniques, energy harvesting communications, massive MIMO systems, cognitive radio and machine learning for communications.

Speech Title: Towards Machine Learning-Enabled Visible Light Communications
Abstract: Visible light communication (VLC) has emerged as a disruptive form of wireless communication. VLC systems can achieve high data rates, low latency and high security. However, performance of VLC in different scenarios significantly depends on several practical considerations such as transmitter nonlinearities, random blockages, random receiver orientation and user mobility. A promising approach to address such issues and deliver a superior performance is machine learning based VLC design. In this talk, we will introduce the application of machine learning techniques for several VLC systems, discuss contemporary results and an outlook on future directions of research.