Linear state estimation via multiple sensors over rate-constrained channels

Abstract:

In this talk we will consider state estimation of a discrete time linear system using multiple sensors, where the sensors quantize their individual innovations (based on their own filtered estimates), which are then combined at the fusion centre to form a global state estimate. We obtain asymptotic expressions for the error covariance (or mean squared error) that relates the system parameters and bit rates used by the different sensors. Numerical results show close agreement with the true mean squared error for quantization at even moderate bit rates. Using dynamic quantization we outline a proof of stability of the estimator for sufficiently high rates. An optimal rate allocation problem amongst the different sensors will also be considered and results presented. This initial study is the first to establish a relationship between rate of quantization and estimation error for linear dynamical system in a multi-terminal setting. Various lines of further investigations will also be discussed.

Presentation Slides

Biography:

Subhrakanti Dey was born in India, in 1968. He received the B.Tech. and M.Tech. degrees from the Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, India, in 1991 and 1993, respectively, and the Ph.D. degree from the Department of Systems Engineering, Research School of Information Sciences and Engineering, Australian National University, Canberra, Australia, in 1996.
He has been with the Department of Electrical and Electronic Engineering, University of Melbourne, Australia , since February 2000, where he is currently a full Professor. From September 1995 to September 1997 and September 1998 to February 2000, he was a postdoctoral Research Fellow with the Department of Systems Engineering, Australian National University. From September 1997 to September 1998, he was a post-doctoral Research Associate with the Institute for Systems Research, University of Maryland, College Park. His current research interests include networked control systems, wireless communications and networks, signal processing for sensor networks, and stochastic and adaptive estimation and control. Prof. Dey currently serves on the Editorial Board of Elsevier Systems and Control Letters. He was also an Associate Editor for the IEEE Transactions on Signal Processing and the IEEE Transactions on Automatic Control. He is a Senior Member of IEEE.