This work presents a novel general regularized distributed solution for the state estimation problem in networked systems. Resting on the graph-based representation of sensor networks and adopting a multivariate least-squares approach, the designed solution exploits the set of the available inter-sensor relative measurements and leverages a general regularization framework, whose parameter selection is shown to control the estimation procedure convergence performance. As confirmed by the numerical results, this new estimation scheme allows ( $i$ ) the extension of other approaches investigated in the literature and ( $ii$ ) the convergence optimization in correspondence to any (undirected) graph modeling the given sensor network.

A General Regularized Distributed Solution for System State Estimation from Relative Measurements

Fabris M.
;
Michieletto G.;Cenedese A.
2022

Abstract

This work presents a novel general regularized distributed solution for the state estimation problem in networked systems. Resting on the graph-based representation of sensor networks and adopting a multivariate least-squares approach, the designed solution exploits the set of the available inter-sensor relative measurements and leverages a general regularization framework, whose parameter selection is shown to control the estimation procedure convergence performance. As confirmed by the numerical results, this new estimation scheme allows ( $i$ ) the extension of other approaches investigated in the literature and ( $ii$ ) the convergence optimization in correspondence to any (undirected) graph modeling the given sensor network.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3410396
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