Date: Monday, April 14^th, 2025

Time: 10:30-11:30  

Location: E10-205, Yungu Campus, Westlake University

Host: Mohamad Sawan, Chair Professor, School of Engineering, Westlake University

Speaker：

Prof. Xiao-Ping (Steven) Zhang

Tsinghua Pengrui Professor

Chair Professor

Tsinghua-Berkeley Shenzhen Institute (TBSl) 

Biography:

Xiao-Ping (Steven) Zhang received the B.S. and Ph.D. degrees from Tsinghua University, in 1992 and 1996, respectively, all in electronic engineering. He holds an MBA in Finance and Economics with Honors from the University of Chicago Booth School of Business. He is Tsinghua Pengrui Chair Professor at Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University. He was the founding Dean of Institute of Data and Information (iDI) at Tsinghua SIGS. He had been with the Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University (Formerly Ryerson University), Toronto, ON, Canada, as a Professor and the Director of the Communication and Signal Processing Applications Laboratory (CASPAL) and has served as the Program Director of Graduate Studies. His research interests include sensor networks and IoT, machine learning/AI/robotics, statistical signal processing, image and multimedia content analysis, and applications in big data, finance, and marketing.

Dr. Zhang is Fellow of the Canadian Academy of Engineering, Fellow of the Engineering Institute of Canada, Fellow of the IEEE, a registered Professional Engineer in Ontario, Canada, and a member of Beta Gamma Sigma Honor Society. He is the general Co-Chair for the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021 and 2027. He is the general co-chair for 2017 GlobalSIP Symposium on Signal and Information Processing for Finance and Business, and the general co-chair for 2019 GlobalSIP Symposium on Signal, Information Processing and AI for Finance and Business. He was an elected Member of the ICME steering committee. He is the general chair for ICME2024 and BioCAS2023. He is Editor-in-Chief for the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING. He is Senior Area Editor for the IEEE TRANSACTIONS ON IMAGE PROCESSING. He served as Senior Area Editor the IEEE TRANSACTIONS ON SIGNAL PROCESSING and Associate Editor for the IEEE TRANSACTIONS ON IMAGE PROCESSING, the IEEE TRANSACTIONS ON MULTIMEDIA, the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, the IEEE TRANSACTIONS ON SIGNAL PROCESSING, and the IEEE SIGNAL PROCESSING LETTERS. He was selected as IEEE Distinguished Lecturer by the IEEE Signal Processing Society and by the IEEE Circuits and Systems Society. 

Abstract:

Multitarget tracking (MTT) is an important component of situation-awareness in many scenarios such as Internet of Things (IoT), UAV networks, and low altitude economy. Existing algorithms mainly focus on tracking based on conventional measurements, e.g., bearings or ranges, in isolation, limiting the accuracy and resolution of MTT, and the related data association is an NP-hard multidimensional assignment problem. In this talk, we present a new one-step MTT framework based on a novel dynamic Bayesian network (DBN), i.e., DBNMTT, by incorporating the physical model information. The new MTT method directly infers target states from the raw measurement data by fusing the array signal model, the signal propagation model, and the motion model, thereby bypasses data association. We treat target states and conventional measurements, such as bearings and target energies as hidden random variables. The posterior joint probability optimization problem is translated into the problem of graphical model learning. In this way, we convert the NP-hard data association problem to a hidden variable probabilistic learning problem informed by physical models. We develop new approximate variational inference based learning algorithms for DBNMTT in both narrow and wideband acoustic sensor array network (ASAN) scenarios. The numerical simulation results show that the proposed algorithms outperform existing MTTs in terms of accuracy, convergence, and computational complexity. Field experiments further verify the feasibility of the new framework.

Contact:
Ms. Linh Chu, School of Engineering

chuyenlinh@westlake.edu.cn