Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Decentralized estimation and control with overlapping input, state, and output decomposition
Automatica (Journal of IFAC)
Parallel Asynchronous Team Algorithms: Convergence and Performance Analysis
IEEE Transactions on Parallel and Distributed Systems
Stochastic analysis and control of real-time systems with random time delays
Automatica (Journal of IFAC)
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback
Automatica (Journal of IFAC)
Decomposition and decentralized control of systems with multi-overlapping structure
Automatica (Journal of IFAC)
Brief paper: Moving-horizon partition-based state estimation of large-scale systems
Automatica (Journal of IFAC)
Distributed estimation via iterative projections with application to power network monitoring
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In this paper a new algorithm for discrete-time overlapping decentralized state estimation of large scale systems is proposed in the form of a multi-agent network based on a combination of local estimators of Kalman filtering type and a dynamic consensus strategy, assuming intermittent observations and communication faults. Under general conditions concerning the agent resources and the network topology, conditions are derived for the convergence to zero of the estimation error mean and for the mean-square estimation error boundedness. A centralized strategy based on minimization of the steady-state mean-square estimation error is proposed for selection of the consensus gains; these gains can also be adjusted by local adaptation schemes. It is also demonstrated that there exists a connection between the network complexity and efficiency of denoising, i.e., of suppression of the measurement noise influence. Several numerical examples serve to illustrate characteristic properties of the proposed algorithm and to demonstrate its applicability to real problems.