A Note on Optimal Distributed State Estimation for Linear Time-Varying Systems
By: Irene Perez-Salesa, Rodrigo Aldana-Lopez, Carlos Sagues
Potential Business Impact:
Makes many computers work together like one smart brain.
In this technical note, we prove that the ODEFTC algorithm constitutes the first optimal distributed state estimator for continuous-time linear time-varying systems subject to stochastic disturbances. Particularly, we formally show that it is able to asymptotically recover the performance, in terms of error covariance of the estimates at each node, of the centralized Kalman-Bucy filter, which is known to be the optimal filter for the considered class of systems. Moreover, we provide a simple sufficient value for the consensus gain to guarantee the stability of the distributed estimator.
Similar Papers
Distributed Adaptive Estimation over Sensor Networks with Partially Unknown Source Dynamics
Systems and Control
Helps sensors share data to learn about things.
Fully Distributed State Estimation for Multi-agent Systems and its Application in Cooperative Localization
Systems and Control
Lets robots know where each other are.
A Framework for Adaptive Stabilisation of Nonlinear Stochastic Systems
Systems and Control
Teaches robots to learn and control unpredictable machines.