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Personal Information:
More >>Associate professor Supervisor of Doctorate Candidates Supervisor of Master's Candidates
Honors and Titles:
2018 elected:Outstanding Instructor of Innovation and Entrepreneurship for College Students of Beijing University of Posts and Telecommunications
Personal Profile
Wang Shengchu, Associate Professor, PhD/Master's Supervisor, earned his PhD from the Department of Electronic Engineering at Tsinghua University. His research focuses on embodied intelligent swarm navigation and positioning, as well as intelligent wireless communication. He is dedicated to integrating modern artificial intelligence with classical signal processing theories to achieve collaborative pose estimation of embodied intelligent agents without navigation infrastructure, and to develop nonlinear MIMO physical layer signal processing technologies for next-generation cellular communications. His related research has been supported by more than ten projects, including the National Natural Science Foundation General/Youth Projects, the National Key R&D Program, National Defense Research Funds, and enterprise-sponsored projects. His research findings have been published in over forty first/corresponding author papers in top-tier journals such as IEEE Transactions on Wireless Communications, IEEE Transactions on Transportation Systems, IEEE Transactions on Communications, and international conferences (see the publication list at https://dblp.org/pid/136/5328.html). He has also been granted seven national invention patents and has one pending. In terms of student mentorship, he has guided three master's students to win National Scholarships, led undergraduate students to achieve second and third prizes in the University Student Innovation and Entrepreneurship Competition, and guided graduate students to secure a second prize in the Innovation and Entrepreneurship Competition.
Educational Experience
Work ExperienceMore>>
Research Group
In the field of collaborative pose estimation, we focus on designing intelligent SLAM odometry systems with front-end multi-sensor fusion and developing back-end algorithms for six-degree-of-freedom (6-DoF) pose recovery.
In the area of nonlinear MIMO signal processing, we explore the use of low-power, low-cost amplitude/phase detectors and other nonlinear components to design RF receiving circuits. Additionally, we develop dedicated baseband signal processing algorithms, including channel estimation and multi-user detection, for nonlinear MIMO systems based on Bayesian approximate message passing algorithms.
Beyond theoretical exploration, the team is also dedicated to hardware system implementation. We build hardware platforms for collaborative pose estimation using sensors such as monocular/binocular/RGBD cameras, LiDAR, UWB, and IMU to validate our pose estimation algorithms. Furthermore, we construct RF receivers using ADI nonlinear components to collect multi-channel observations for nonlinear MIMO systems, enabling the verification of our baseband algorithms.