Qi Wu received the B.S. degree from East China Normal University, Shanghai, China, and the Ph.D. Degree from Shanghai Jiao Tong University, Shanghai, China, both in Electrical Engineering, in 2004 and 2009, respectively.
He joined the faculty of School of Electronics and Information Engineering, Beihang University, Beijing, China, in 2009, now he is a Full Professor. During 2011 and 2012, he was a Visiting Scholar in the Department of Electrical Engineering, University of California, Los Angeles. During 2014 and 2016, he was an Alexander von Humboldt Fellow in the Institute of Electromagnetic Theory, Technical University of Hamburg, Germany. He has authored over 40 journal papers, two books, and holds 20 patents as the first inventor. His research interests include broadband antennas, computational electromagnetics, and related EMC topics.
Dr. Wu received the Young Scientist Award from the International Union of Radio Science (URSI) in 2011, the Nominee Award for Excellent Doctoral Dissertation from the National Minister of Education in 2012, Young Scientist Award of APEMC in 2016, and Excellent Researcher from Chinese institute of Electronics in 2020.

Title: Stochastic Quantification of Phased Array with Random Errors

Abstract:
Various random errors existed in phased array, e.g., T/R module, fabrication tolerance, and mechanical deformation. It is important to understand effects of those random errors, and find possible ways to alleviate those effects.
In this talk, we will introduce two methods for stochastic quantification of phased array with random errors. The first one takes advantage of the information of array manifold, and divide a large array into smaller groups. Each group can be efficiently evaluated through the polynomial chaos expansion (PCE) method. Both the independent and dependent random variables are treated by this method. The second method uses unified neutral network (UNN) for modeling the spatial distributions of the radiated field from phased array. An efficient preprocessing procedure is introduced to improve the convergence of UNN. This UNN model fits phased arrays with similar elements but different array size.
Finally, some measurement results of digital phase shifters are demonstrated and discussed.