We describe a consensus-based distributed filtering algorithm for linear systems with a parametrized gain and show that when the parameter becomes large the error covariance at each node becomes arbitrarily close to the error covariance of the optimal centralized Kalman filter. The result concerns distributed estimation over a connected un-directed or directed graph and for static configurations it only requires to exchange the estimates among adjacent nodes. A comparison with related approaches confirms the theoretical results and shows that the method can be applied to a wide range of distributed estimation problems.

Asymptotically optimal consensus-based distributed filtering of continuous-time linear systems / Battilotti, Stefano; Cacace, Filippo; D'Angelo, Massimiliano; Germani, Alfredo. - In: AUTOMATICA. - ISSN 0005-1098. - 122:(2020). [10.1016/j.automatica.2020.109189]

Asymptotically optimal consensus-based distributed filtering of continuous-time linear systems

Stefano Battilotti;Massimiliano d’Angelo;Alfredo Germani
2020

Abstract

We describe a consensus-based distributed filtering algorithm for linear systems with a parametrized gain and show that when the parameter becomes large the error covariance at each node becomes arbitrarily close to the error covariance of the optimal centralized Kalman filter. The result concerns distributed estimation over a connected un-directed or directed graph and for static configurations it only requires to exchange the estimates among adjacent nodes. A comparison with related approaches confirms the theoretical results and shows that the method can be applied to a wide range of distributed estimation problems.
2020
Continuous-time filters; Kalman filtering; Distributed filtering; Consensus filters
01 Pubblicazione su rivista::01a Articolo in rivista
Asymptotically optimal consensus-based distributed filtering of continuous-time linear systems / Battilotti, Stefano; Cacace, Filippo; D'Angelo, Massimiliano; Germani, Alfredo. - In: AUTOMATICA. - ISSN 0005-1098. - 122:(2020). [10.1016/j.automatica.2020.109189]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1436316
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