A Resilient Approach to Distributed Filter Design for Time-Varying Systems Under Stochastic Nonlinearities and Sensor Degradation

This paper is concerned with the distributed filtering problem for a class of discrete time-varying systems with stochastic nonlinearities and sensor degradation over a finite horizon. A two-step distributed filter algorithm is proposed where the sensor nodes collaboratively estimate the states of t...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 65; no. 5; pp. 1300 - 1309
Main Authors Qinyuan Liu, Zidong Wang, Xiao He, Ghinea, Gheorghita, Alsaadi, Fuad E.
Format Journal Article
LanguageEnglish
Published IEEE 01.03.2017
Subjects
Algorithm design and analysis
Covariance matrices
Degradation
distributed filtering
Estimation
Resilient filter
sensor degradation
Signal processing algorithms
stochastic nonlinearity
Topology
Wireless sensor networks
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Summary:This paper is concerned with the distributed filtering problem for a class of discrete time-varying systems with stochastic nonlinearities and sensor degradation over a finite horizon. A two-step distributed filter algorithm is proposed where the sensor nodes collaboratively estimate the states of the plant by exploiting the information from both the local and the neighboring nodes. The goal of this paper is to design the distributed filters over a wireless sensor network subject to given sporadic communication topology. Moreover, a resilient operation is guaranteed to suppress random perturbations on the actually implemented filter gains. An upper bound is first derived for the filtering error covariance by utilizing an inductive method and such an upper bound is subsequently minimized via iteratively solving a quadratic optimization problem. To account for the topological information of the sensor networks, a novel matrix simplification technique is utilized to preserve the sparsity of the gain matrices in accordance with the given topology, and the analytical parameterization is obtained for the gain matrices of the desired suboptimal filter. Furthermore, a sufficient condition is established to guarantee the mean-square boundedness of the estimation errors. Numerical simulation is carried out to verify the effectiveness of the proposed filtering algorithm.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2016.2634541