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Distributed Kalman filtering for time-varying discrete sequential systems. (English) Zbl 1406.93341
Summary: Discrete sequential system (DSS) consisting of different dynamical subsystems is a sequentially-connected dynamical system, and has found applications in many fields such as automation processes and series systems. However, few results are focused on the state estimation of DSSs. In this paper, the distributed Kalman filtering problem is studied for time-varying DSSs with Gaussian white noises. A locally optimal distributed estimator is designed in the linear minimum variance sense, and a stability condition is derived such that the mean square error of the distributed estimator is bounded. An illustrative example is given to demonstrate the effectiveness of the proposed methods.

93E11 Filtering in stochastic control theory
93E10 Estimation and detection in stochastic control theory
93A14 Decentralized systems
60H40 White noise theory
93-04 Software, source code, etc. for problems pertaining to systems and control theory
93A15 Large-scale systems
93D05 Lyapunov and other classical stabilities (Lagrange, Poisson, \(L^p, l^p\), etc.) in control theory
Full Text: DOI
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