报告时间：2025.12.04 10:00~11:30
报告地点：无线谷A1楼1306
报告人： Dr. Jiajun He
Abstract: Localization is essential for enabling a wide range of location-based services, including target tracking, digital twins, autonomous driving, and extended reality. In this presentation, a new analytical paradigm is introduced for evaluating the fundamental limits of localization systems by leveraging the concepts of target localizability and the stochastic Cramér-Rao bound (CRLB). Compared to traditional analyses that assume fixed network geometries, the proposed framework incorporates the randomness of spatial configurations, offering insight into how geometry fundamentally impacts localization performance. To illustrate the practical value of this approach, several representative case studies are discussed, including: 1) cell-free massive MIMO (CF-mMIMO)-based RSS localization, 2) multi-static radar localization, and 3) integrated localization, mapping, and communication (LMAC). Lastly, this talk concludes with a discussion on the real-world deployment of localization systems using software-defined radio (SDR), highlighting the practical feasibility of the proposed localization schemes.
Bio: Jiajun He received the Ph.D. degree from The City University of Hong Kong, Hong Kong, in 2024. Since June 2024, he has been a Research Fellow at Queen’s University Belfast, Belfast, U.K. His research interests include source localization, radar signal processing, cell-free massive MIMO, embedded system design, and deep learning.
