Distributed weighted least-squares estimation with fast convergence for large-scale systems

Marelli, Damián Edgardo; Fu, Minyue

Title: Distributed weighted least-squares estimation with fast convergence for large-scale systems
Creator: Marelli, Damián Edgardo; Fu, Minyue
Relation: Automatica Vol. 51, Issue January 2015, p. 27-39
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2014.10.077
Publisher: Elsevier
Resource Type: journal article
Date: 2015
Description: In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods.
Subject: distributed estimation; distributed state estimation; large scale optimization; sensor network; networked control
Identifier: http://hdl.handle.net/1959.13/1334557
Identifier: uon:27321
Identifier: ISSN:0005-1098
Language: eng
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