  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.

Systems and Control;Systems and Control