Paper Info
Title
Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks.
Abstract
This paper is concerned with the distributed recursive filtering problem for a class of discrete time-delayed stochastic systems subject to both uniform quantization and deception attack effects on the measurement outputs. The target plant is disturbed by the multiplicative as well as additive white noises. A novel distributed filter is designed where the available innovations are from not only the individual sensor but also its neighboring ones according to the given topology. Attention is focused on the design of a distributed recursive filter such that, in the simultaneous presence of time-delays, deception attacks and uniform quantization effects, an upper bound for the filtering error covariance is guaranteed and subsequently minimized by properly designing the filter parameters via a gradient-based method at each sampling instant. Furthermore, by utilizing the mathematical induction, a sufficient condition is established to ensure the asymptotic boundedness of the sequence of the error covariance. Finally, a simulation example is utilized to illustrate the usefulness of the proposed design scheme of distributed filters.
Year
DOI
Venue
2017
10.1016/j.automatica.2016.12.026
Automatica
Keywords
Field
DocType
Sensor networks,Distributed filtering,Recursive filtering,Deception attacks,Uniform quantization
Control theory,Upper and lower bounds,Filter (signal processing),Mathematical induction,Filtering problem,Recursive filter,Wireless sensor network,Recursion,Mathematics,Covariance
Journal
Volume
Issue
ISSN
78
C
0005-1098
Citations 
PageRank 
References 
95
1.91
29
Authors
4
Name
Order
Citations
PageRank
Derui Ding1122746.37
Zidong Wang211003578.11
Daniel W.C. Ho35311285.38
Guoliang Wei4130971.09