Distributed Kalman filtering for time-varying discrete sequential systems

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.

[1]  Aleksandar Haber,et al.  Moving Horizon Estimation for Large-Scale Interconnected Systems , 2013, IEEE Transactions on Automatic Control.

[2]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[4]  Shinkyu Park,et al.  Design of Distributed LTI Observers for State Omniscience , 2017, IEEE Transactions on Automatic Control.

[5]  Milos S. Stankovic,et al.  Consensus Based Overlapping Decentralized Estimator , 2009, IEEE Transactions on Automatic Control.

[6]  C. Sanders,et al.  Specific structures for large-scale state estimation algorithms having information exchange , 1978 .

[7]  Valery A. Ugrinovskii,et al.  Distributed robust estimation over randomly switching networks using H∞ consensus , 2015, Autom..

[8]  Francis J. Doyle,et al.  A distributed state estimation and control algorithm for plantwide processes , 2003, IEEE Trans. Control. Syst. Technol..

[9]  Marcello Farina,et al.  Moving-horizon partition-based state estimation of large-scale systems , 2024, Autom..

[10]  James Lam,et al.  New approach to mixed H/sub 2//H/sub /spl infin// filtering for polytopic discrete-time systems , 2005, IEEE Transactions on Signal Processing.

[11]  Marcello Farina,et al.  Plug-and-play state estimation and application to distributed output-feedback model predictive control , 2015, Eur. J. Control.

[12]  José M. F. Moura,et al.  Distributing the Kalman Filter for Large-Scale Systems , 2007, IEEE Transactions on Signal Processing.

[13]  Zidong Wang,et al.  H∞ filtering for uncertain stochastic time-delay systems with sector-bounded nonlinearities , 2008, Autom..

[14]  C. Sanders,et al.  A new class of decentralized filters for interconnected systems , 1974 .

[15]  Minyue Fu,et al.  Distributed weighted least-squares estimation with fast convergence for large-scale systems , 2015, 52nd IEEE Conference on Decision and Control.

[16]  Daniel W. C. Ho,et al.  Networked Fusion Estimation With Bounded Noises , 2017, IEEE Transactions on Automatic Control.

[17]  T. Cline,et al.  Near-optimal state estimation for interconnected systems , 1973, CDC 1973.

[18]  Mariusz Piotr Hetmańczyk,et al.  The visualization of discrete sequential systems , 2009 .

[19]  Ming Jian Zuo,et al.  Selective Maintenance for Multistate Series Systems With S-Dependent Components , 2016, IEEE Transactions on Reliability.

[20]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .