Title: Kalman Consensus Filtering in the Presence of Data Packet Drops
Abstract: We study Kalman consensus filtering (KCF) over wireless sensor networks in the presence of data packet drops. The optimal estimator is derived, assuming the TCP-like protocol. The stationary Kalman filter minimizes the average error variance, designed by solving the stabilizing solution to the modified algebraic Riccati equation (MARE). The existence of the stabilizing solution to the MARE is analyzed, and an equivalent condition in terms of some simple LMIs is obtained. Finally the KCF is studied, and a necessary and sufficient condition is obtained for the MS stability of the KCF, illustrate by a numerical example from the literature.
Short Bio: Guoxiang Gu received the Ph.D. degree in electrical engineering from the University of Minnesota, Minneapolis, in 1988. From 1988 to 1990, he was with the Department of Electrical Engineering, Wright State University, Dayton, Ohio, as a Visiting Assistant Professor. Since 1990, he joined Louisiana State University (LSU), Baton Rouge, where he is currently a Professor of Electrical and Computer Engineering. His research interests include networked control systems, system identification, and statistical signal processing. He authored two books, and published over 70 archive journal papers, plus numerous book chapters, and conference papers. He has held visiting positions at Wright-Patterson Air Force Base, in Hong Kong University of Science and Technology, and in Southwest Jiaotong University of P.R. China. He served as an associate editor for the IEEE Transactions on Automatic Control from 1999 to 2001, SIAM Journal on Control and Optimization from 2006 to 2009, and Automatica from 2006 to 2012, and will serve again as an associate editor for IEEE Transactions on Automatic Control from 2018-2020. He is presently the F. Hugh Coughlin/CLECO distinguished professor of electrical engineering at LSU, and Fellow of IEEE.