Distributed input and state estimation for linear discrete-time systems

Alireza Esna Ashari 1 Alain Y. Kibangou 1 Federica Garin 1
1 NECS - Networked Controlled Systems
Inria Grenoble - Rhône-Alpes, GIPSA-DA - Département Automatique
Abstract : This paper provides a solution for distributed input and state estimation, simultaneously. A set of sensors with the capability of exchanging information is used to collect data from a discrete-time system. Various distributed implementations of Kalman filter have already been developed to track system states in such a setup when the system is subject to noise with known stochastic properties. However, practical systems might be subject to unknown input signals as well as stochastic noise, which leads to a biased state estimation. This study proposes new distributed filter that calculate state estimation in the presence of unknown inputs. In addition, the filter provides an estimation of the unknown inputs. A consensus-based distributed estimation algorithm is proposed in this paper. The algorithm gives an optimal unbiased minimum variance estimation if perfect consensus is achieved. Simulation results show the efficiency of the method.
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Contributor : Federica Garin <>
Submitted on : Monday, August 27, 2012 - 11:46:47 AM
Last modification on : Wednesday, July 25, 2018 - 1:19:43 AM

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Alireza Esna Ashari, Alain Y. Kibangou, Federica Garin. Distributed input and state estimation for linear discrete-time systems. 51st IEEE Conference on Decision and Control (CDC 2012), Dec 2012, Maui (Hawaii), United States. pp.782-787, ⟨10.1109/CDC.2012.6426366⟩. ⟨hal-00725518⟩

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