Distributed H∞ State Estimation Over a Filtering Network With Time-Varying and Switching Topology and Partial Information Exchange
File version
Author(s)
Han, Qing-Long
Liu, Yurong
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
Size
File type(s)
Location
License
Abstract
This paper is concerned with the distributed H∞ state estimation for a discrete-time target linear system over a filtering network with time-varying and switching topology and partial information exchange. Both filtering network topology switching and partial information exchange between filters are simultaneously considered in the filter design. The topology under consideration evolves not only over time but also by an event switch which is assumed to be subject to a nonhomogeneous Markov chain. The probability transition matrix of the nonhomogeneous Markov chain is time-varying. In the filter information exchange, partial state estimation information and channel noise are simultaneously considered. In order to design such a switching filtering network with partial information exchange, stochastic Markov stability theory is developed. The switching topology-dependent filters are derived to guarantee an optimal H∞ disturbance rejection attenuation level for the estimation disagreement of the filtering network. It is shown that the addressed H∞ state estimation problem is turned into a switching topology-dependent optimal problem. The distributed filtering problem with complete information exchanges from its neighbors is also investigated. An illustrative example is given to show the applicability of the obtained results.
Journal Title
IEEE Transactions on Cybernetics
Conference Title
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
This publication has been entered into Griffith Research Online as an Advanced Online Version.
Access the data
Related item(s)
Subject
Artificial intelligence
Applied mathematics