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

TL;DR

This paper introduces a flexible regularized distributed method for system state estimation in sensor networks, improving convergence and extending existing approaches through a graph-based least-squares framework.

Contribution

It proposes a novel regularization framework that enhances distributed state estimation by controlling convergence and generalizing prior methods.

Findings

The method effectively extends existing approaches.

It optimizes convergence based on network topology.

Numerical results confirm improved estimation accuracy.

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.