应我校电信学院微波工程系及2022年中国微波周组委会邀请，国际著名电磁学专家新加坡国立大学Xudong Chen教授（IEEE Fellow）将于2022年8月14日访问讲学和交流研讨，欢迎感兴趣的老师和同学参加。讲座内容和时间地点安排如下：
讲座题目：Target Classification Using Physics-Assisted Machine learning in 77 GHz Automotive Radar
讲座时间：2022年8月14日10:15-10:45
讲座地点：Zoom ID: 764 480 8366，Password 202208
摘要：
Autonomous driving technology is effective to reduce traffic accidents, and object classification is a key problem for achieving the full autonomy. This paper proposes a high-accuracy and efficient classification method with physics-assisted machine learning techniques on frequency-modulated-continuous-wave (FMCW) radar. We first establish the novel mapping relationship from physical space to range-Doppler (R-D) image based on wave propagation theory, by which four physical features that effectively capture the kinematic and geometrical characteristics of targets, including speed, total reflectivity, area, and incidence angle, are extracted from R-D image. Then, a multi-layer perceptron with a single hidden layer is employed to realize the classification. Since the above-mentioned four physical features, derived from wave physics, are chosen as the input of the neural network, our classifier does not work in a black-box way. The computational complexity of the whole classifier is the same as that of a 2D FFT, which guarantees a real-time operation. The proposed classifier is applied to automotive radar system, where road targets are to be classified into five categories, including pedestrian, bike, sedan, truck/bus, and other static objects. Real-world data obtained from 77-GHz FMCW radars are provided for the validation, where the proposed physics-assisted classifier turns out to outperform the state of the art in automotive radar application. The overall accuracy by the real data is about 99% even with complex multiple-target cases.
Xudong Chen教授简介
Xudong Chen (Fellow, IEEE) received the B.S. and M.S. degrees from Zhejiang University, Hangzhou, China, in 1999 and 2001, respectively, and the Ph.D. degree from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 2005. Since 2005, he has been with the National University of Singapore, Singapore, where he is a Professor.He has published 160 journal articles on inverse scattering problems, material parameter retrieval, microscopy, and optical encryption. He has authored the book Computational Methods for Electromagnetic Inverse Scattering (Wiley-IEEE, 2018). His research interests include mainly electromagnetic wave theories and applications, with a focus on inverse problems and computational imaging. He is recently working on mm-wave imaging algorithms and solving inverse problems via machine learning. Dr. Chen was a recipient of the Young Scientist Award by the Union Radio Scientifique Internationale in 2010 and the Ulrich L. Rohde Innovative Conference Paper Award at IEEE International Conference on Computational Electromagnetics (ICCEM) 2019. He was an Associate Editor of the IEEE Transactions on Microwave Theory and Techniques from 2015 to 2019 and is an Associate Editor of IEEE Transactions on Geoscience and Remote Sensing and the IEEE Journal of Electromagnetics, RFand Microwave in Medicine and Biology. He has been members of organizing committees of more than 10 conferences, serving as the General Chair, the Technical Programme Committee (TPC) Chair, the Award Committee Chair, etc. He was the Chair of IEEE Singapore Microwave Theory and Technique (MTT)/AP Joint Chapter in 2018. He is a fellow of Electromagnetics Academy.
