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Distributed \(H_\infty\)-consensus filtering in sensor networks with multiple missing measurements: the finite-horizon case. (English) Zbl 1204.93122
Summary: This paper is concerned with a new distributed \(H_\infty\)-consensus filtering problem over a finite-horizon for sensor networks with multiple missing measurements. The so-called \(H_\infty\)-consensus performance requirement is defined to quantify bounded consensus regarding the filtering errors (agreements) over a finite-horizon. A set of random variables are utilized to model the probabilistic information missing phenomena occurring in the channels from the system to the sensors. A sufficient condition is first established in terms of a set of Difference Linear Matrix Inequalities (DLMIs) under which the expected \(H_\infty\)-consensus performance constraint is guaranteed. Given the measurements and estimates of the system state and its neighbors, the filter parameters are then explicitly parameterized by means of the solutions to a certain set of DLMIs that can be computed recursively. Subsequently, two kinds of robust distributed \(H_\infty\)-consensus filters are designed for the system with norm-bounded uncertainties and polytopic uncertainties. Finally, two numerical simulation examples are used to demonstrate the effectiveness of the proposed distributed filters design scheme.

93E11 Filtering in stochastic control theory
93B51 Design techniques (robust design, computer-aided design, etc.)
Full Text: DOI
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