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Electrical Engineering and Systems Science > Systems and Control

arXiv:2108.03172 (eess)
[Submitted on 6 Aug 2021 (v1), last revised 16 Nov 2021 (this version, v2)]

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

Authors:Marco Fabris, Giulia Michieletto, Angelo Cenedese
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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.
Comments: 6 pages, 1 figure. Index Terms: Sensor networks, Estimation, Network analysis and control
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2108.03172 [eess.SY]
  (or arXiv:2108.03172v2 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2108.03172
Journal reference: in IEEE Control Systems Letters, pp. 1580-1585, Nov 2021
Related DOI: https://doi.org/10.1109/LCSYS.2021.3126258

Submission history

From: Marco Fabris [view email]
[v1] Fri, 6 Aug 2021 15:41:00 UTC (4,283 KB)
[v2] Tue, 16 Nov 2021 10:10:16 UTC (474 KB)
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