title: Distributed weighted least-squares estimation with fast convergence for large-scale systems.
abstract: 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.
authors: School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia ; Acoustics Research Institute, Austrian Academy of Sciences, Austria.
authors: Damián Edgardo
authors: DE
authors: Marelli
authors: School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia ; Department of Control Science and Engineering and State Key Laboratory of Industrial Control Technology, Zhejiang University, 388 Yuhangtang Road Hangzhou, Zhejiang Province, 310058, PR China.
authors: Minyue
authors: M
authors: Fu
keywords: Distributed estimation; Distributed state estimation; Large scale optimization; Networked control; Sensor network
references: Automatica (Oxf). 2015 Jan;51:27-39
references: 25641976