- 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
- Rights
- © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
- Language
- eng
- Full Text
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