A Proximal Point Approach for Distributed System State Estimation
Abstract
System state estimation constitutes a key problem in several applications involving multi-agent system architectures. This rests upon the estimation of the state of each agent in the group, which is supposed to access only relative measurements w.r.t. some neighbors state. Exploiting the standard least-squares paradigm, the system state estimation task is faced in this work by deriving a distributed Proximal Point-based iterative scheme. This solution entails the emergence of interesting connections between the structural properties of the stochastic matrices describing the system dynamics and the convergence behavior toward the optimal estimate. A deep analysis of such relations is provided, jointly with a further discussion on the penalty parameter that characterizes the Proximal Point approach.
Cite
@article{arxiv.2103.11325,
title = {A Proximal Point Approach for Distributed System State Estimation},
author = {Marco Fabris and Giulia Michieletto and Angelo Cenedese},
journal= {arXiv preprint arXiv:2103.11325},
year = {2021}
}
Comments
6 pages, 2 figures, 1 table, manuscript n 3555, \c{opyright} 2020 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND